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Master Adjacent Disciplineshttp://www.effectiveengineer.com/blog/master-adjacent-disciplines
T-shaped skillshttps://en.wikipedia.org/wiki/T-shaped_skills
Data Scientists Should Be More End-to-Endhttps://eugeneyan.com/writing/end-to-end-data-science/
https://github.com/StudyWithJeffrey/learning#develop-a-business-acumen
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Book: Learn to Earn: A Beginner's Guide to the Basics of Investing and Businesshttps://www.goodreads.com/book/show/817589.Learn_to_Earn
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Facebook: Digital marketing: get startedhttps://learn.fb.com/skillset/marketing-started
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Love, art and stories: decoded | The Age of A.I.https://www.youtube.com/watch?v=Kr1fmKVY3cA
The 'Space Architects' of Mars | The Age of A.I.https://www.youtube.com/watch?v=lIvrIKaNCRE
Will a robot take my job? | The Age of A.I.https://www.youtube.com/watch?v=f2aocKWrPG8
Saving the world one algorithm at a time | The Age of A.I.https://www.youtube.com/watch?v=0wy4u34fii4
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Youtube: Gradient Dissent Podcasthttps://www.youtube.com/playlist?list=PLD80i8An1OEEb1jP0sjEyiLG8ULRXFob_
DeepChem creator Bharath Ramsundar on using deep learning for molecules and medicine discoveryhttps://www.youtube.com/watch?v=GnkpVjp117k
ML Research and Production Pipelines with Chip Huyenhttps://www.youtube.com/watch?v=6adNHwE5PHY
Product Management for AI with Peter Skomorochhttps://www.youtube.com/watch?v=hSyb3xEvCrI
Slow down and change one thing at a time - Advancing AI research with Josh Tobinhttps://www.youtube.com/watch?v=G6AgmZ6_R3U
Societal Impacts of Artificial Intelligence with Miles Brundagehttps://www.youtube.com/watch?v=O2ya8M72y0U
Deep Reinforcement Learning and Robotics with Peter Welinderhttps://www.youtube.com/watch?v=1VI3xTh-TMA
Machine learning across industries with Vicki Boykishttps://www.youtube.com/watch?v=pOnRSYSNuXI
Designing ML models for millions of consumer robots - Angela Bassa and Danielle Deanhttps://www.youtube.com/watch?v=W55uO4gIlQ4
Building trustworthy AI systems and combating potential malicious use – A conversation w/ Jack Clarkhttps://www.youtube.com/watch?v=nv_f1Gk8Ybk
Rachael Tatman - Conversational A.I. and Linguisticshttps://www.youtube.com/watch?v=n_CTGZSq4m0
Nicolas Koumchatzky - Machine Learning in Production for Self Driving Carshttps://www.youtube.com/watch?v=NbiG8ZuRsqU
Brandon Rohrer - Machine Learning in Production for Robotshttps://www.youtube.com/watch?v=_Ot35PspXw4
https://github.com/StudyWithJeffrey/learning#understand-data-ethics-better
Practical Data Ethicshttp://ethics.fast.ai/
Lesson 1: Disinformationhttp://ethics.fast.ai/videos/?lesson=1
Lesson 2: Bias & Fairnesshttp://ethics.fast.ai/videos/?lesson=2
Lesson 3: Ethical Foundations & Practical Toolshttp://ethics.fast.ai/videos/?lesson=3
Lesson 4: Privacy and surveillancehttp://ethics.fast.ai/videos/?lesson=4
Lesson 4 continued: Privacy and surveillancehttp://ethics.fast.ai/videos/?lesson=5
Lesson 5.1: The problem with metricshttp://ethics.fast.ai/videos/?lesson=6
Lesson 5.2: Our Ecosystem, Venture Capital, & Hypergrowthhttp://ethics.fast.ai/videos/?lesson=7
Lesson 5.3: Losing the Forest for the Trees, guest lecture by Ali Alkhatibhttp://ethics.fast.ai/videos/?lesson=8
Lesson 6: Algorithmic Colonialism, and Next Stepshttp://ethics.fast.ai/videos/?lesson=9
https://github.com/StudyWithJeffrey/learning#be-able-to-annotate-data-efficiently
Youtube: Snorkel: Dark Data and Machine Learning - Christopher Réhttps://www.youtube.com/watch?v=yu15Nf5eJEE
Youtube: Training a NER Model with Prodigy and Transfer Learninghttps://youtu.be/59BKHO_xBPA
Youtube: Training a New Entity Type with Prodigy – annotation powered by active learninghttps://youtu.be/l4scwf8KeIA
https://github.com/StudyWithJeffrey/learning#be-able-to-manipulate-data-with-numpy
Datacamp: Intro to Python for Data Sciencehttps://www.datacamp.com/courses/intro-to-python-for-data-science
Pluralsight: Working with Multidimensional Data Using NumPyhttps://www.pluralsight.com/courses/numpy-working-with-multidimensional-data
https://github.com/StudyWithJeffrey/learning#be-able-to-manipulate-data-with-pandas
Datacamp: pandas Foundationshttps://www.datacamp.com/courses/pandas-foundations
Datacamp: Pandas Joins for Spreadsheet Usershttps://www.datacamp.com/courses/pandas-joins-for-spreadsheet-users
Datacamp: Manipulating DataFrames with pandashttps://www.datacamp.com/courses/manipulating-dataframes-with-pandas
Datacamp: Merging DataFrames with pandashttps://www.datacamp.com/courses/merging-dataframes-with-pandas
Datacamp: Data Manipulation with pandashttps://www.datacamp.com/courses/data-manipulation-with-pandas
Datacamp: Optimizing Python Code with pandashttps://www.datacamp.com/courses/optimizing-python-code-with-pandas
Datacamp: Streamlined Data Ingestion with pandashttps://www.datacamp.com/courses/streamlined-data-ingestion-with-pandas
Datacamp: Analyzing Marketing Campaigns with pandashttps://www.datacamp.com/courses/analyzing-marketing-campaigns-with-pandas
Article: Modern Pandashttps://tomaugspurger.github.io
Modern Pandas (Part 1)https://tomaugspurger.github.io/modern-1-intro.html
Modern Pandas (Part 2)https://tomaugspurger.github.io/method-chaining.html
Modern Pandas (Part 3)https://tomaugspurger.github.io/modern-3-indexes.html
Modern Pandas (Part 4)https://tomaugspurger.github.io/modern-4-performance.html
Modern Pandas (Part 5)https://tomaugspurger.github.io/modern-5-tidy.html
Modern Pandas (Part 6)https://tomaugspurger.github.io/modern-6-visualization.html
Modern Pandas (Part 7)https://tomaugspurger.github.io/modern-7-timeseries.html
Modern Pandas (Part 8)https://tomaugspurger.github.io/modern-8-scaling.html
https://github.com/StudyWithJeffrey/learning#be-able-to-manipulate-data-in-spreadsheets
Datacamp: Spreadsheet basicshttps://www.datacamp.com/courses/spreadsheet-basics
Datacamp: Data Analysis with Spreadsheetshttps://www.datacamp.com/courses/data-analysis-with-spreadsheets
Datacamp: Intermediate Spreadsheets for Data Sciencehttps://www.datacamp.com/courses/intermediate-spreadsheets-for-data-science
Datacamp: Pivot Tables with Spreadsheetshttps://www.datacamp.com/courses/pivot-tables-with-spreadsheets
Datacamp: Data Visualization in Spreadsheetshttps://www.datacamp.com/courses/data-visualization-in-spreadsheets
Datacamp: Introduction to Statistics in Spreadsheetshttps://www.datacamp.com/courses/statistics-in-spreadsheets
Datacamp: Conditional Formatting in Spreadsheetshttps://www.datacamp.com/courses/conditional-formatting-in-spreadsheets
Datacamp: Marketing Analytics in Spreadsheetshttps://www.datacamp.com/courses/marketing-analytics-in-spreadsheets
Datacamp: Error and Uncertainty in Spreadsheetshttps://www.datacamp.com/courses/error-and-uncertainty-in-spreadsheets
edX: Analyzing and Visualizing Data with Excelhttps://www.edx.org/course/analyzing-visualizing-data-excel-microsoft-dat206x-7
https://github.com/StudyWithJeffrey/learning#be-able-to-manipulate-data-in-databases
Codecademy: SQL Trackhttps://www.codecademy.com/courses/learn-sql
Datacamp: Intro to SQL for Data Sciencehttps://www.datacamp.com/courses/intro-to-sql-for-data-science
Datacamp: Introduction to MongoDB in Pythonhttps://www.datacamp.com/courses/introduction-to-using-mongodb-for-data-science-with-python
Datacamp: Intermediate SQLhttps://www.datacamp.com/courses/intermediate-sql
Datacamp: Exploratory Data Analysis in SQLhttps://www.datacamp.com/courses/sql-for-exploratory-data-analysis
Datacamp: Joining Data in PostgreSQLhttps://www.datacamp.com/courses/joining-data-in-postgresql
Datacamp: Querying with TransactSQLhttps://www.datacamp.com/courses/querying-with-transact-sql
Datacamp: Introduction to Databases in Pythonhttps://www.datacamp.com/courses/introduction-to-relational-databases-in-python
Datacamp: Reporting in SQLhttps://www.datacamp.com/courses/reporting-in-sql
Datacamp: Applying SQL to Real-World Problemshttps://www.datacamp.com/courses/applying-sql-to-real-world-problems
Datacamp: Analyzing Business Data in SQLhttps://www.datacamp.com/courses/analyzing-business-data-in-sql
Datacamp: Data-Driven Decision Making in SQLhttps://www.datacamp.com/courses/data-driven-decision-making-with-sql
Datacamp: Database Designhttps://www.datacamp.com/courses/database-design
Udacity: SQL for Data Analysishttps://www.udacity.com/course/sql-for-data-analysis--ud198
Udacity: Intro to relational databasehttps://www.udacity.com/course/intro-to-relational-databases--ud197
Udacity: Database Systems Concepts & Designhttps://www.udacity.com/course/database-systems-concepts-design--ud150
https://github.com/StudyWithJeffrey/learning#be-able-to-use-the-command-line
Codecademy: Learn the Command Linehttps://www.codecademy.com/learn/learn-the-command-line
Datacamp: Introduction to Shell for Data Sciencehttps://www.datacamp.com/courses/introduction-to-shell-for-data-science
Datacamp: Data Processing in Shellhttps://www.datacamp.com/courses/data-processing-in-shell
LaunchSchool: Introduction to Commandlinehttps://launchschool.com/books/command_line
Learn Enough Command Line to be dangeroushttp://www.learnenough.com/command-line-tutorial
Thoughtbot: Mastering the Shellhttps://thoughtbot.com/upcase/mastering-the-shell
Thoughtbot: tmuxhttps://thoughtbot.com/upcase/tmux
Udacity: Linux Command Line Basicshttps://www.udacity.com/course/linux-command-line-basics--ud595
Udacity: Linux Web Servershttps://www.udacity.com/courses/ud299
Udacity: Shell Workshophttps://www.udacity.com/course/shell-workshop--ud206
Udacity: Web Tooling & Automationhttps://www.udacity.com/course/web-tooling-automation--ud892
Web Bos: Command Line Power Userhttps://www.youtube.com/watch?v=DP218aBHm1Q&list=PLu8EoSxDXHP7tXPJp5ZmUpuT7sFvrswzf&index=2
https://github.com/StudyWithJeffrey/learning#be-able-to-import-data-from-multiple-sources
Datacamp: Importing Data in Python (Part 2)https://www.datacamp.com/courses/importing-data-in-python-part-2
Datacamp: Web Scraping in Pythonhttps://www.datacamp.com/courses/web-scraping-with-python
https://github.com/StudyWithJeffrey/learning#be-able-to-perform-feature-engineering
Article: Preparing data for a machine learning modelhttps://www.jeremyjordan.me/preparing-data-for-a-machine-learning-model/
Article: Feature selection for a machine learning modelhttps://www.jeremyjordan.me/feature-selection/
Article: Learning from imbalanced datahttps://www.jeremyjordan.me/imbalanced-data/
Article: Hacker's Guide to Data Preparation for Machine Learninghttps://www.curiousily.com/posts/hackers-guide-to-data-preparation-for-machine-learning/
Article: Practical Guide to Handling Imbalanced Datasetshttps://www.curiousily.com/posts/practical-guide-to-handling-imbalanced-datasets/
Datacamp: Analyzing Social Media Data in Pythonhttps://www.datacamp.com/courses/analyzing-social-media-data-in-python
Datacamp: Dimensionality Reduction in Pythonhttps://www.datacamp.com/courses/dimensionality-reduction-in-python
Datacamp: Preprocessing for Machine Learning in Pythonhttps://www.datacamp.com/courses/preprocessing-for-machine-learning-in-python
Datacamp: Data Types for Data Sciencehttps://www.datacamp.com/courses/data-types-for-data-science
Datacamp: Cleaning Data in Pythonhttps://www.datacamp.com/courses/cleaning-data-in-python
Datacamp: Feature Engineering for Machine Learning in Pythonhttps://www.datacamp.com/courses/feature-engineering-for-machine-learning-in-python
Datacamp: Importing & Managing Financial Data in Pythonhttps://www.datacamp.com/courses/importing-managing-financial-data-in-python
Datacamp: Manipulating Time Series Data in Pythonhttps://www.datacamp.com/courses/manipulating-time-series-data-in-python
Datacamp: Working with Geospatial Data in Pythonhttps://www.datacamp.com/courses/working-with-geospatial-data-in-python
Datacamp: Analyzing IoT Data in Pythonhttps://www.datacamp.com/courses/analyzing-iot-data-in-python
Datacamp: Dealing with Missing Data in Pythonhttps://www.datacamp.com/courses/dealing-with-missing-data-in-python
Datacamp: Exploratory Data Analysis in Pythonhttps://www.datacamp.com/courses/exploratory-data-analysis-in-python
edX: Data Science Essentialshttps://www.edx.org/course/data-science-essentials-microsoft-dat203-1x-5
Google: Feature Engineeringhttps://www.coursera.org/learn/feature-engineering
Udacity: Creating an Analytical Datasethttps://www.udacity.com/course/creating-an-analytical-dataset--ud977
https://github.com/StudyWithJeffrey/learning#be-able-to-experiment-in-notebook
Pluralsight: Getting Started with Jupyter Notebook and Pythonhttps://www.pluralsight.com/courses/jupyter-notebook-python
https://github.com/StudyWithJeffrey/learning#be-able-to-visualize-data
Datacamp: Introduction to Data Visualization with Pythonhttps://www.datacamp.com/courses/introduction-to-data-visualization-with-python
Datacamp: Introduction to Seabornhttps://www.datacamp.com/courses/introduction-to-seaborn
Datacamp: Introduction to Matplotlibhttps://www.datacamp.com/courses/introduction-to-matplotlib
Datacamp: Intermediate Data Visualization with Seabornhttps://www.datacamp.com/courses/data-visualization-with-seaborn
Datacamp: Visualizing Time Series Data in Pythonhttps://www.datacamp.com/courses/visualizing-time-series-data-in-python
Datacamp: Improving Your Data Visualizations in Pythonhttps://www.datacamp.com/courses/improving-your-data-visualizations-in-python
Datacamp: Visualizing Geospatial Data in Pythonhttps://www.datacamp.com/courses/visualizing-geospatial-data-in-python
Datacamp: Interactive Data Visualization with Bokehhttps://www.datacamp.com/courses/interactive-data-visualization-with-bokeh
Udacity: Data Visualization in Tableauhttps://www.udacity.com/course/data-visualization-in-tableau--ud1006
Youtube: Jake VanderPlas - Exploratory Data Visualization with Vega, Vega-Lite, and Altair - PyCon 2018https://www.youtube.com/watch?v=ms29ZPUKxbU
UWData: Data Visualization Curriculumhttps://github.com/uwdata/visualization-curriculum
https://github.com/StudyWithJeffrey/learning#be-able-to-to-read-research-papers
Paper: A Neural Probabilistic Language Modelhttp://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf
Paper: Efficient Estimation of Word Representations in Vector Spacehttps://arxiv.org/pdf/1301.3781.pdf
Paper: Sequence to Sequence Learning with Neural Networkshttps://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf
Paper: Neural Machine Translation by Jointly Learning to Align and Translatehttps://arxiv.org/abs/1409.0473
Paper: Attention Is All You Needhttps://arxiv.org/abs/1706.03762
Paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understandinghttps://arxiv.org/abs/1810.04805
Paper: XLNet: Generalized Autoregressive Pretraining for Language Understandinghttps://arxiv.org/abs/1906.08237
Paper: Synonyms Based Term Weighting Scheme: An Extension to TF.IDFhttps://www.researchgate.net/publication/306362767_Synonyms_Based_Term_Weighting_Scheme_An_Extension_to_TFIDF
Paper: RoBERTa: A Robustly Optimized BERT Pretraining Approachhttps://arxiv.org/abs/1907.11692
Paper: GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandinghttps://www.nyu.edu/projects/bowman/glue.pdf
Paper: Amazon.com Recommendations Item-to-Item Collaborative Filteringhttps://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf
Paper: Collaborative Filtering for Implicit Feedback Datasetshttp://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=34AEEE06F0C2428083376C26C71D7CFF?doi=10.1.1.167.5120&rep=rep1&type=pdf
Paper: BPR: Bayesian Personalized Ranking from Implicit Feedbackhttps://arxiv.org/pdf/1205.2618.pdf
Paper: Factorization Machineshttps://cseweb.ucsd.edu/classes/fa17/cse291-b/reading/Rendle2010FM.pdf
Paper: Wide & Deep Learning for Recommender Systemshttps://arxiv.org/pdf/1606.07792.pdf
Paper: Neural Factorization Machines for Sparse Predictive Analyticshttps://l.facebook.com/l.php?u=https%3A%2F%2Farxiv.org%2Fpdf%2F1708.05027.pdf&h=AT3VuDk1rSqAkgo1x79wl9FXtb7SFMT01B1MXLMvp0O8syX2BuHYB70EJkMwVngQtShj0yTTn6laoRQ3I7StkJQJ9j1b8DiHM7gXNv7dWvL9S_khSF4wWZA9No70BhewiggJ8a8Pa0jTnq4_ppOIsk-qDYVkyJM5QuoSSg
Paper: Multiword Expressions: A Pain in the Neck for NLPhttp://lingo.stanford.edu/pubs/WP-2001-03.pdf
Paper: PyTorch: An Imperative Style, High-Performance Deep Learning Libraryhttps://arxiv.org/pdf/1912.01703.pdf
Paper: ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONShttps://arxiv.org/pdf/1909.11942.pdf
Paper: Self-supervised Visual Feature Learning with Deep Neural Networks: A Surveyhttps://arxiv.org/abs/1902.06162
Paper: A Simple Framework for Contrastive Learning of Visual Representationshttps://arxiv.org/pdf/2002.05709.pdf
Paper: Self-Supervised Learning of Pretext-Invariant Representationshttps://arxiv.org/abs/1912.01991
Paper: FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencehttps://arxiv.org/abs/2001.07685
Paper: Self-Labelling via Simultaneous Clustering and Representation Learninghttps://www.robots.ox.ac.uk/~vgg/research/self-label/
Paper: A Survey on Contextual Embeddingshttps://arxiv.org/abs/2003.07278v1
Paper: A survey on Semi-, Self- and Unsupervised Techniques in Image Classificationhttps://arxiv.org/abs/2002.08721
Paper: Shortcut Learning in Deep Neural Networkshttps://arxiv.org/abs/2004.07780
Paper: Multi-document Summarization by using TextRank and Maximal Marginal Relevance for Text in Bahasa Indonesiahttps://www.researchgate.net/publication/338940065_Multi-document_Summarization_by_using_TextRank_and_Maximal_Marginal_Relevance_for_Text_in_Bahasa_Indonesia
Paper: Train Once, Test Anywhere: Zero-Shot Learning for Text Classificationhttps://arxiv.org/abs/1712.05972
Paper: Zero-shot Text Classification With Generative Language Modelshttps://arxiv.org/abs/1912.10165
Paper: How to Fine-Tune BERT for Text Classification?https://arxiv.org/abs/1905.05583
Paper: Universal Sentence Encoderhttps://arxiv.org/abs/1803.11175
Paper: Enriching Word Vectors with Subword Informationhttps://arxiv.org/abs/1607.04606
Paper: Deep Learning Based Text Classification: A Comprehensive Reviewhttps://arxiv.org/abs/2004.03705
Paper: Beyond Accuracy: Behavioral Testing of NLP models with CheckListhttps://arxiv.org/abs/2005.04118
Paper: Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networkshttp://deeplearning.net/wp-content/uploads/2013/03/pseudo_label_final.pdf
Paper: Temporal Ensembling for Semi-Supervised Learninghttps://arxiv.org/abs/1610.02242
Paper: Boosting Self-Supervised Learning via Knowledge Transferhttps://arxiv.org/abs/1805.00385
https://github.com/StudyWithJeffrey/learning#be-able-to-model-problems-mathematically
3Blue1Brown: Essence of Calculushttps://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr
The Essence of Calculus, Chapter 1https://www.youtube.com/watch?v=WUvTyaaNkzM
The paradox of the derivative | Essence of calculus, chapter 2https://www.youtube.com/watch?v=9vKqVkMQHKk
Derivative formulas through geometry | Essence of calculus, chapter 3https://www.youtube.com/watch?v=S0_qX4VJhMQ
Visualizing the chain rule and product rule | Essence of calculus, chapter 4https://www.youtube.com/watch?v=YG15m2VwSjA
What's so special about Euler's number e? | Essence of calculus, chapter 5https://www.youtube.com/watch?v=m2MIpDrF7Es
Implicit differentiation, what's going on here? | Essence of calculus, chapter 6https://www.youtube.com/watch?v=qb40J4N1fa4
Limits, L'Hôpital's rule, and epsilon delta definitions | Essence of calculus, chapter 7https://www.youtube.com/watch?v=kfF40MiS7zA
Integration and the fundamental theorem of calculus | Essence of calculus, chapter 8https://www.youtube.com/watch?v=rfG8ce4nNh0
What does area have to do with slope? | Essence of calculus, chapter 9https://www.youtube.com/watch?v=FnJqaIESC2s
Higher order derivatives | Essence of calculus, chapter 10https://www.youtube.com/watch?v=BLkz5LGWihw
Taylor series | Essence of calculus, chapter 11https://www.youtube.com/watch?v=3d6DsjIBzJ4
What they won't teach you in calculushttps://www.youtube.com/watch?v=CfW845LNObM
3Blue1Brown: Essence of linear algebrahttps://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab
Vectors, what even are they? | Essence of linear algebra, chapter 1https://www.youtube.com/watch?v=fNk_zzaMoSs
Linear combinations, span, and basis vectors | Essence of linear algebra, chapter 2https://www.youtube.com/watch?v=k7RM-ot2NWY
Linear transformations and matrices | Essence of linear algebra, chapter 3https://www.youtube.com/watch?v=kYB8IZa5AuE
Matrix multiplication as composition | Essence of linear algebra, chapter 4https://www.youtube.com/watch?v=XkY2DOUCWMU
Three-dimensional linear transformations | Essence of linear algebra, chapter 5https://www.youtube.com/watch?v=rHLEWRxRGiM
The determinant | Essence of linear algebra, chapter 6https://www.youtube.com/watch?v=Ip3X9LOh2dk
Inverse matrices, column space and null space | Essence of linear algebra, chapter 7https://www.youtube.com/watch?v=uQhTuRlWMxw
Nonsquare matrices as transformations between dimensions | Essence of linear algebra, chapter 8https://www.youtube.com/watch?v=v8VSDg_WQlA
Dot products and duality | Essence of linear algebra, chapter 9https://www.youtube.com/watch?v=LyGKycYT2v0
Cross products | Essence of linear algebra, Chapter 10https://www.youtube.com/watch?v=eu6i7WJeinw
Cross products in the light of linear transformations | Essence of linear algebra chapter 11https://www.youtube.com/watch?v=BaM7OCEm3G0
Cramer's rule, explained geometrically | Essence of linear algebra, chapter 12https://www.youtube.com/watch?v=jBsC34PxzoM
Change of basis | Essence of linear algebra, chapter 13https://www.youtube.com/watch?v=P2LTAUO1TdA
Eigenvectors and eigenvalues | Essence of linear algebra, chapter 14https://www.youtube.com/watch?v=PFDu9oVAE-g
Abstract vector spaces | Essence of linear algebra, chapter 15https://www.youtube.com/watch?v=TgKwz5Ikpc8
3Blue1Brown: Neural networkshttps://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi
But what is a Neural Network? | Deep learning, chapter 1https://www.youtube.com/watch?v=aircAruvnKk
Gradient descent, how neural networks learn | Deep learning, chapter 2https://www.youtube.com/watch?v=IHZwWFHWa-w
What is backpropagation really doing? | Deep learning, chapter 3https://www.youtube.com/watch?v=Ilg3gGewQ5U
Backpropagation calculus | Deep learning, chapter 4https://www.youtube.com/watch?v=tIeHLnjs5U8
Article: A Visual Tour of Backpropagationhttps://blog.jinay.dev/posts/backprop/
Article: Relearning Matrices as Linear Functionshttps://www.dhruvonmath.com/2018/12/31/matrices/
Article: You Could Have Come Up With Eigenvectors - Here's Howhttps://www.dhruvonmath.com/2019/02/25/eigenvectors/
Article: PageRank - How Eigenvectors Power the Algorithm Behind Google Searchhttps://www.dhruvonmath.com/2019/03/20/pagerank/
Article: Interactive Visualization of Why Eigenvectors Matterhttps://www.dhruvonmath.com/2020/07/26/who-cares-about-eigenvectors/
Article: Cross-Entropy and KL Divergencehttps://medium.com/swlh/cross-entropy-and-kl-divergence-522d9f71bd3d
Article: Why Randomness Is Information?https://medium.com/swlh/why-randomness-is-information-f2468966b29d
Article: Basic Probability Theoryhttps://medium.com/swlh/probability-for-machine-learning-and-data-science-cccd4f4f1df1
Book: Basics of Linear Algebra for Machine Learninghttps://machinelearningmastery.com/linear_algebra_for_machine_learning/
Datacamp: Foundations of Probability in Pythonhttps://www.datacamp.com/courses/foundations-of-probability-in-python
Datacamp: Statistical Thinking in Python (Part 1)https://www.datacamp.com/courses/statistical-thinking-in-python-part-1
Datacamp: Statistical Thinking in Python (Part 2)https://www.datacamp.com/courses/statistical-thinking-in-python-part-2
Datacamp: Statistical Simulation in Pythonhttps://www.datacamp.com/courses/statistical-simulation-in-python
edX: Essential Statistics for Data Analysis using Excelhttps://www.edx.org/course/essential-statistics-data-analysis-using-microsoft-dat222x-1
Computational Linear Algebra for Codershttps://github.com/fastai/numerical-linear-algebra
Khan Academy: Precalculushttps://www.khanacademy.org/math/precalculus
Khan Academy: Probabilityhttps://www.khanacademy.org/mission/probability
Khan Academy: Differential Calculushttps://www.khanacademy.org/mission/differential-calculus
Khan Academy: Multivariable Calculushttps://www.khanacademy.org/math/multivariable-calculus
Khan Academy: Linear Algebrahttps://www.khanacademy.org/math/linear-algebra
MIT: 18.06 Linear Algebra (Professor Strang)https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
1. The Geometry of Linear Equationshttps://www.youtube.com/watch?v=J7DzL2_Na80
2. Elimination with Matrices.https://www.youtube.com/watch?v=QVKj3LADCnA
3. Multiplication and Inverse Matriceshttps://www.youtube.com/watch?v=FX4C-JpTFgY
4. Factorization into A = LUhttps://www.youtube.com/watch?v=MsIvs_6vC38
5. Transposes, Permutations, Spaces R^nhttps://www.youtube.com/watch?v=JibVXBElKL0
6. Column Space and Nullspacehttps://www.youtube.com/watch?v=8o5Cmfpeo6g
9. Independence, Basis, and Dimensionhttps://www.youtube.com/watch?v=yjBerM5jWsc
10. The Four Fundamental Subspaceshttps://www.youtube.com/watch?v=nHlE7EgJFds
11. Matrix Spaces; Rank 1; Small World Graphshttps://www.youtube.com/watch?v=2IdtqGM6KWU
14. Orthogonal Vectors and Subspaceshttps://www.youtube.com/watch?v=YzZUIYRCE38
15. Projections onto Subspaceshttps://www.youtube.com/watch?v=Y_Ac6KiQ1t0
16. Projection Matrices and Least Squareshttps://www.youtube.com/watch?v=osh80YCg_GM
17. Orthogonal Matrices and Gram-Schmidthttps://www.youtube.com/watch?v=0MtwqhIwdrI
21. Eigenvalues and Eigenvectorshttps://www.youtube.com/watch?v=cdZnhQjJu4I
22. Diagonalization and Powers of Ahttps://www.youtube.com/watch?v=13r9QY6cmjc
24. Markov Matrices; Fourier Serieshttps://www.youtube.com/watch?v=lGGDIGizcQ0
25. Symmetric Matrices and Positive Definitenesshttps://www.youtube.com/watch?v=UCc9q_cAhho
27. Positive Definite Matrices and Minimahttps://www.youtube.com/watch?v=vF7eyJ2g3kU
29. Singular Value Decompositionhttps://www.youtube.com/watch?v=TX_vooSnhm8
30. Linear Transformations and Their Matriceshttps://www.youtube.com/watch?v=Ts3o2I8_Mxc
31. Change of Basis; Image Compressionhttps://www.youtube.com/watch?v=0h43aV4aH7I
33. Left and Right Inverses; Pseudoinversehttps://www.youtube.com/watch?v=Go2aLo7ZOlU
StatQuest: Statistics Fundamentalshttps://www.youtube.com/playlist?list=PLblh5JKOoLUK0FLuzwntyYI10UQFUhsY9
StatQuest: Histograms, Clearly Explainedhttps://www.youtube.com/watch?v=qBigTkBLU6g
StatQuest: What is a statistical distribution?https://www.youtube.com/watch?v=oI3hZJqXJuc
StatQuest: The Normal Distribution, Clearly Explained!!!https://www.youtube.com/watch?v=rzFX5NWojp0
Statistics Fundamentals: Population Parametershttps://www.youtube.com/watch?v=vikkiwjQqfU
Statistics Fundamentals: The Mean, Variance and Standard Deviationhttps://www.youtube.com/watch?v=SzZ6GpcfoQY
StatQuest: What is a statistical model?https://www.youtube.com/watch?v=yQhTtdq_y9M
StatQuest: Sampling A Distributionhttps://www.youtube.com/watch?v=XLCWeSVzHUU
Hypothesis Testing and The Null Hypothesishttps://www.youtube.com/watch?v=0oc49DyA3hU
Alternative Hypotheses: Main Ideas!!!https://www.youtube.com/watch?v=5koKb5B_YWo
p-values: What they are and how to interpret themhttps://www.youtube.com/watch?v=vemZtEM63GY
How to calculate p-valueshttps://www.youtube.com/watch?v=JQc3yx0-Q9E
p-hacking: What it is and how to avoid it!https://www.youtube.com/watch?v=HDCOUXE3HMM
Statistical Power, Clearly Explained!!!https://www.youtube.com/watch?v=Rsc5znwR5FA
Power Analysis, Clearly Explained!!!https://www.youtube.com/watch?v=VX_M3tIyiYk
Covariance and Correlation Part 1: Covariancehttps://www.youtube.com/watch?v=qtaqvPAeEJY
Covariance and Correlation Part 2: Pearson's Correlationhttps://www.youtube.com/watch?v=xZ_z8KWkhXE
StatQuest: R-squared explainedhttps://www.youtube.com/watch?v=2AQKmw14mHM
The Central Limit Theoremhttps://www.youtube.com/watch?v=YAlJCEDH2uY
StatQuickie: Standard Deviation vs Standard Errorhttps://www.youtube.com/watch?v=A82brFpdr9g
StatQuest: The standard errorhttps://www.youtube.com/watch?v=XNgt7F6FqDU
Bam!!! Clearly Explained!!!https://www.youtube.com/watch?v=i4iUvjsGCMc
StatQuest: Technical and Biological Replicateshttps://www.youtube.com/watch?v=Exk0OoRG0PQ
StatQuest - Sample Size and Effective Sample Size, Clearly Explainedhttps://www.youtube.com/watch?v=67zCIqdeXpo
Bar Charts Are Better than Pie Chartshttps://www.youtube.com/watch?v=RiEZ_hEf96A
StatQuest: Boxplots, Clearly Explainedhttps://www.youtube.com/watch?v=fHLhBnmwUM0
StatQuest: Logs (logarithms), clearly explainedhttps://www.youtube.com/watch?v=VSi0Z04fWj0
StatQuest: Confidence Intervalshttps://www.youtube.com/watch?v=TqOeMYtOc1w
StatQuickie: Thresholds for Significancehttps://www.youtube.com/watch?v=KEofcJ1tfkI
StatQuickie: Which t test to usehttps://www.youtube.com/watch?v=nnBJeb_I-q8
StatQuest: One or Two Tailed P-Valueshttps://www.youtube.com/watch?v=bsZGt-caXO4
The Binomial Distribution and Test, Clearly Explained!!!https://www.youtube.com/watch?v=J8jNoF-K8E8
StatQuest: Quantiles and Percentiles, Clearly Explained!!!https://www.youtube.com/watch?v=IFKQLDmRK0Y
StatQuest: Quantile-Quantile Plots (QQ plots), Clearly Explainedhttps://www.youtube.com/watch?v=okjYjClSjOg
StatQuest: Quantile Normalizationhttps://www.youtube.com/watch?v=ecjN6Xpv6SE
StatQuest: Probability vs Likelihoodhttps://www.youtube.com/watch?v=pYxNSUDSFH4
StatQuest: Maximum Likelihood, clearly explained!!!https://www.youtube.com/watch?v=XepXtl9YKwc
Maximum Likelihood for the Exponential Distribution, Clearly Explained! V2.0https://www.youtube.com/watch?v=p3T-_LMrvBc
Why Dividing By N Underestimates the Variancehttps://www.youtube.com/watch?v=sHRBg6BhKjI
Maximum Likelihood for the Binomial Distribution, Clearly Explained!!!https://www.youtube.com/watch?v=4KKV9yZCoM4
Maximum Likelihood For the Normal Distribution, step-by-step!https://www.youtube.com/watch?v=Dn6b9fCIUpM
StatQuest: Odds and Log(Odds), Clearly Explained!!!https://www.youtube.com/watch?v=ARfXDSkQf1Y
StatQuest: Odds Ratios and Log(Odds Ratios), Clearly Explained!!!https://www.youtube.com/watch?v=8nm0G-1uJzA
Live 2020-04-20!!! Expected Valueshttps://www.youtube.com/watch?v=fU2PuYKsr6M
Udacity: Algebra Reviewhttps://www.udacity.com/course/intro-algebra-review--ma004
Udacity: Differential Equations in Actionhttps://www.udacity.com/course/differential-equations-in-action--cs222
Udacity: Eigenvectors and Eigenvalueshttps://www.udacity.com/course/eigenvectors-and-eigenvalues--ud104
Udacity: Linear Algebra Refresherhttps://www.udacity.com/course/linear-algebra-refresher-course--ud953
Udacity: Statisticshttps://www.udacity.com/course/statistics--st095
Udacity: Intro to Descriptive Statisticshttps://www.udacity.com/course/intro-to-descriptive-statistics--ud827
Udacity: Intro to Inferential Statisticshttps://www.udacity.com/course/intro-to-inferential-statistics--ud201
Youtube: Principal Component Analysis (PCA) - THE MATH YOU SHOULD KNOW!https://www.youtube.com/watch?v=9oSkUej63yk
Youtube: Support Vector Machines - THE MATH YOU SHOULD KNOWhttps://www.youtube.com/watch?v=05VABNfa1ds
Youtube: The Kernel Trick - THE MATH YOU SHOULD KNOW!https://www.youtube.com/watch?v=wBVSbVktLIY
Youtube: Logistic Regression - THE MATH YOU SHOULD KNOW!https://www.youtube.com/watch?v=YMJtsYIp4kg
Youtube: But what is a Neural Network? - THE MATH YOU SHOULD KNOW!https://www.youtube.com/watch?v=oB3gmT8GAgI
https://github.com/StudyWithJeffrey/learning#be-able-to-structure-machine-learning-projects
Article: Organizing machine learning projects: project management guidelineshttps://www.jeremyjordan.me/ml-projects-guide/
Article: Building machine learning products: a problem well-defined is a problem half-solved.https://www.jeremyjordan.me/ml-requirements/
Coursera: Structuring Machine Learning Projectshttps://www.coursera.org/learn/machine-learning-projects?specialization=deep-learning
Datacamp: Conda Essentialshttps://www.datacamp.com/courses/conda-essentials
Datacamp: Conda for Building & Distributing Packageshttps://www.datacamp.com/courses/conda-for-building-distributing-packages
Datacamp: Creating Robust Python Workflowshttps://www.datacamp.com/courses/creating-robust-python-workflows
Datacamp: Software Engineering for Data Scientists in Pythonhttps://www.datacamp.com/courses/software-engineering-for-data-scientists-in-python
Datacamp: Designing Machine Learning Workflows in Pythonhttps://www.datacamp.com/courses/designing-machine-learning-workflows-in-python
Datacamp: Object-Oriented Programming in Pythonhttps://www.datacamp.com/courses/object-oriented-programming-in-python
Datacamp: Command Line Automation in Pythonhttps://www.datacamp.com/courses/command-line-automation-in-python
Datacamp: Introduction to Data Engineeringhttps://www.datacamp.com/courses/introduction-to-data-engineering
Datacamp: Experimental Design in Pythonhttps://www.datacamp.com/courses/experimental-design-in-python
Full Stack Deep Learning Bootcamp: March 2019https://fullstackdeeplearning.com/march2019
Lecture 1: Introduction to Deep Learninghttps://youtu.be/5AjG5OPQuBM
Lecture 2: Setting Up Machine Learning Projectshttps://youtu.be/tBUK1_cHu-8
Lecture 3: Introduction to the Text Recognizer Projecthttps://youtu.be/mmlvGLSXKLc
Lecture 4: Infrastructure and Toolinghttps://youtu.be/f6jAz1zyrDI
Lecture 5: Tracking Experimentshttps://youtu.be/Eiz1zcqrqw0
Lecture 6: Data Managementhttps://youtu.be/T5jv8-xZhZI
Lecture 7: Machine Learning Teamshttps://youtu.be/Qb3RhwNb4EM
Lecture 9: Lukas Biewaldhttps://youtu.be/25_kBogrzrs
Lecture 10: Troubleshooting Deep Neural Networkshttps://youtu.be/GwGTwPcG0YM
Lecture 11: Labs 6-9: Detection, Data Labeling, Testing and Deploymenthttps://youtu.be/JTSwQu0OyGs
Lecture 12: Testing and Deploymenthttps://youtu.be/nu7h1zdKPd0
Lecture 13: Research Directionshttps://youtu.be/vF7UgqaegVI
Lecture 14: Jeremy Howardhttps://youtu.be/hZd3X_nGdew
Lecture 15: Richard Socherhttps://youtu.be/yvMgcLKuvVg
Guest Lecture - Chip Huyen - Machine Learning Interviews - Full Stack Deep Learninghttps://youtu.be/pli1K75PSa8
MIT: The Missing Semester of CS Educationhttps://www.youtube.com/playlist?list=PLyzOVJj3bHQuloKGG59rS43e29ro7I57J
Lecture 1: Course Overview + The Shell (2020)https://www.youtube.com/watch?v=Z56Jmr9Z34Q
Lecture 2: Shell Tools and Scripting (2020)https://www.youtube.com/watch?v=kgII-YWo3Zw
Lecture 3: Editors (vim) (2020)https://www.youtube.com/watch?v=a6Q8Na575qc
Lecture 4: Data Wrangling (2020)https://www.youtube.com/watch?v=sz_dsktIjt4
Lecture 5: Command-line Environment (2020)https://www.youtube.com/watch?v=e8BO_dYxk5c
Lecture 6: Version Control (git) (2020)https://www.youtube.com/watch?v=2sjqTHE0zok
Lecture 7: Debugging and Profiling (2020)https://www.youtube.com/watch?v=l812pUnKxME
Lecture 8: Metaprogramming (2020)https://www.youtube.com/watch?v=_Ms1Z4xfqv4
Lecture 9: Security and Cryptography (2020)https://www.youtube.com/watch?v=tjwobAmnKTo
Lecture 10: Potpourri (2020)https://www.youtube.com/watch?v=JZDt-PRq0uo
Lecture 11: Q&A (2020)https://www.youtube.com/watch?v=Wz50FvGG6xU
Treehouse: Object Oriented Pythonhttps://teamtreehouse.com/library/objectoriented-python-2
Treehouse: Setup Local Python Environmenthttps://teamtreehouse.com/library/setting-utorialtorialp-a-local-python-environment-windows
Udacity: Writing READMEshttps://www.udacity.com/course/writing-readmes--ud777
Youtube: Weights and Biases Tutorialhttps://www.youtube.com/playlist?list=PLD80i8An1OEE0xs5BjKpCBdm0aaDI00U9
Youtube: MLOps Tutorialshttps://www.youtube.com/playlist?list=PL7WG7YrwYcnDBDuCkFbcyjnZQrdskFsBz
MLOps Tutorial #1: Intro to Continuous Integration for MLhttps://youtu.be/9BgIDqAzfuA?list=PL7WG7YrwYcnDBDuCkFbcyjnZQrdskFsBz
MLOps Tutorial #2: When data is too big for Githttps://youtu.be/kZKAuShWF0s?list=PL7WG7YrwYcnDBDuCkFbcyjnZQrdskFsBz
MLOps Tutorial #3: Track ML models with Git & GitHub Actionshttps://youtu.be/xPncjKH6SPk?list=PL7WG7YrwYcnDBDuCkFbcyjnZQrdskFsBz
https://github.com/StudyWithJeffrey/learning#be-able-to-utilize-version-control
Article: Mastering Git Stash Workflowhttps://dev.to/yankee/mastering-git-stash-workflow-223
Codecademy: Learn Githttps://www.codecademy.com/learn/learn-git
Code School: Git Realhttps://www.pluralsight.com/courses/code-school-git-real
Datacamp: Introduction to Git for Data Sciencehttps://www.datacamp.com/courses/introduction-to-git-for-data-science
Learn enough git to be dangeroushttp://learnenough.com/git-tutorial
Thoughtbot: Mastering Githttps://thoughtbot.com/upcase/mastering-git
Udacity: GitHub & Collaborationhttps://www.udacity.com/course/github-collaboration--ud456
Udacity: How to Use Git and GitHubhttps://www.udacity.com/course/how-to-use-git-and-github--ud775
Udacity: Version Control with Githttps://www.udacity.com/course/version-control-with-git--ud123
https://github.com/StudyWithJeffrey/learning#be-familiar-with-a-breadth-of-models-and-algorithms
Article: Label Smoothing Explained using Microsoft Excelhttps://amaarora.github.io/2020/07/18/label-smoothing.html
Article: Naive Bayes classificationhttps://www.jeremyjordan.me/naive-bayes-classification/
Article: Linear regressionhttps://www.jeremyjordan.me/linear-regression/
Article: Polynomial regressionhttps://www.jeremyjordan.me/polynomial-regression/
Article: Logistic regressionhttps://www.jeremyjordan.me/logistic-regression/
Article: Decision treeshttps://www.jeremyjordan.me/decision-trees/
Article: K-nearest neighborshttps://www.jeremyjordan.me/k-nearest-neighbors/
Article: Support Vector Machineshttps://www.jeremyjordan.me/support-vector-machines/
Article: Random forestshttps://www.jeremyjordan.me/random-forests/
Article: Boosted treeshttps://www.jeremyjordan.me/boosted-trees/
Article: Neural networks: activation functionshttps://www.jeremyjordan.me/neural-networks-activation-functions/
Article: Neural networks: training with backpropagationhttps://www.jeremyjordan.me/neural-networks-training/
Article: Gradient descenthttps://www.jeremyjordan.me/gradient-descent/
Article: Setting the learning rate of your neural networkhttps://www.jeremyjordan.me/nn-learning-rate/
Article: Deep neural networks: preventing overfittinghttps://www.jeremyjordan.me/deep-neural-networks-preventing-overfitting/
Article: Normalizing your data (specifically, input and batch normalization)https://www.jeremyjordan.me/batch-normalization/
Article: Batch Normalizationhttps://e2eml.school/batch_normalization.html
Article: Baidu Deep Voice explained: Part 1 — the Inference Pipelinehttps://blog.athelas.com/paper-1-baidus-deep-voice-675a323705df
Article: Baidu Deep Voice explained Part 2 — Traininghttps://blog.athelas.com/baidu-deep-voice-explained-part-2-training-810e87d20047
Article: Hacker's Guide to Fundamental Machine Learning Algorithms with Pythonhttps://www.curiousily.com/posts/hackers-guide-to-fundamental-machine-learning-algorithms/
Article: Are Deep Neural Networks Dramatically Overfitted?https://lilianweng.github.io/lil-log/2019/03/14/are-deep-neural-networks-dramatically-overfitted.html
Article: Attention? Attention!https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html
Article: How to Explain the Prediction of a Machine Learning Model?https://lilianweng.github.io/lil-log/2017/08/01/how-to-explain-the-prediction-of-a-machine-learning-model.html
Article: Neural Network from scratch-part 1https://theaisummer.com/Neural_Network_from_scratch/
Article: Neural Network from scratch-part 2https://theaisummer.com/Neural_Network_from_scratch_part2/
Article: Explain Neural Arithmetic Logic Units (NALU)https://theaisummer.com/NALU/
Article: Predict Bitcoin price with Long sort term memory Networks (LSTM)https://theaisummer.com/Bitcon_prediction_LSTM/
Article: Graph Neural Networks - An overviewhttps://theaisummer.com/Graph_Neural_Networks/
Article: Deep Learning Algorithms - The Complete Guidehttps://theaisummer.com/Deep-Learning-Algorithms/
AWS: Semantic Segmentation Explainedhttps://www.aws.training/learningobject/video?id=27238
AWS: The Elements of Data Sciencehttps://www.aws.training/learningobject/wbc?id=26598
AWS: Understanding Neural Networkshttps://www.aws.training/learningobject/video?id=27233
Book: Pattern Recognition and Machine Learninghttps://www.goodreads.com/book/show/55881.Pattern_Recognition_and_Machine_Learning
Coursera: Neural Networks and Deep Learninghttps://www.coursera.org/learn/neural-networks-deep-learning
Datacamp: AI Fundamentalshttps://www.datacamp.com/courses/fundamentals-of-ai
Datacamp: Kaggle Competitionhttps://www.datacamp.com/courses/winning-a-kaggle-competition-in-python
Datacamp: Extreme Gradient Boosting with XGBoosthttps://www.datacamp.com/courses/extreme-gradient-boosting-with-xgboost
Datacamp: Introduction to PySparkhttps://www.datacamp.com/courses/introduction-to-pyspark
Datacamp: Building Recommendation Engines with PySparkhttps://www.datacamp.com/courses/recommendation-engines-in-pyspark
Datacamp: Foundations of Predictive Analytics in Python (Part 1)https://www.datacamp.com/courses/foundations-of-predictive-analytics-in-python-part-1
Datacamp: Foundations of Predictive Analytics in Python (Part 2)https://www.datacamp.com/courses/foundations-of-predictive-analytics-in-python-part-2
Datacamp: Ensemble Methods in Pythonhttps://www.datacamp.com/courses/ensemble-methods-in-python
Datacamp: HR Analytics in Python: Predicting Employee Churnhttps://www.datacamp.com/courses/hr-analytics-in-python-predicting-employee-churn
Datacamp: Predicting Customer Churn in Pythonhttps://www.datacamp.com/courses/predicting-customer-churn-in-python
Elements of AIhttps://www.elementsofai.com
edX: Principles of Machine Learninghttps://www.edx.org/course/principles-machine-learning-microsoft-dat203-2x-5
edX: Data Science Essentialshttps://www.edx.org/course/data-science-essentials-microsoft-dat203-1x-5
edX: Implementing Predictive Analytics with Spark in Azure HDInsighthttps://www.edx.org/course/implementing-predictive-analytics-spark-microsoft-dat202-3x-2
DeepMind: Inefficient Data Efficiencyhttps://www.facebook.com/wdeepvision2020/videos/893497114486588/
DeepMind: DeepMind x UCL | Deep Learning Lecture Series 2020https://www.youtube.com/playlist?list=PLqYmG7hTraZCDxZ44o4p3N5Anz3lLRVZF
DeepMind x UCL | Deep Learning Lectures | 1/12 | Intro to Machine Learning & AIhttps://www.youtube.com/watch?v=7R52wiUgxZI
DeepMind x UCL | Deep Learning Lectures | 2/12 | Neural Networks Foundationshttps://www.youtube.com/watch?v=FBggC-XVF4M
DeepMind x UCL | Deep Learning Lectures | 3/12 | Convolutional Neural Networks for Image Recognitionhttps://www.youtube.com/watch?v=shVKhOmT0HE
DeepMind x UCL | Deep Learning Lectures | 4/12 | Advanced Models for Computer Visionhttps://www.youtube.com/watch?v=_aUq7lmMfxo
DeepMind x UCL | Deep Learning Lectures | 5/12 | Optimization for Machine Learninghttps://www.youtube.com/watch?v=kVU8zTI-Od0
DeepMind x UCL | Deep Learning Lectures | 6/12 | Sequences and Recurrent Networkshttps://www.youtube.com/watch?v=87kLfzmYBy8
DeepMind x UCL | Deep Learning Lectures | 7/12 | Deep Learning for Natural Language Processinghttps://www.youtube.com/watch?v=8zAP2qWAsKg
DeepMind x UCL | Deep Learning Lectures | 8/12 | Attention and Memory in Deep Learninghttps://www.youtube.com/watch?v=AIiwuClvH6k
DeepMind x UCL | Deep Learning Lectures | 9/12 | Generative Adversarial Networkshttps://www.youtube.com/watch?v=wFsI2WqUfdA
DeepMind x UCL | Deep Learning Lectures | 10/12 | Unsupervised Representation Learninghttps://www.youtube.com/watch?v=f0s-uvvXvWg
DeepMind x UCL | Deep Learning Lectures | 11/12 | Modern Latent Variable Modelshttps://www.youtube.com/watch?v=7Pcvdo4EJeo
DeepMind x UCL | Deep Learning Lectures | 12/12 | Responsible Innovationhttps://www.youtube.com/watch?v=MhNcWxUs-PQ
Fast.ai: Deep Learning for Coder (2020)https://course.fast.ai/
Lesson 1https://course.fast.ai/videos/?lesson=1
Lesson 2https://course.fast.ai/videos/?lesson=2
Lesson 3https://course.fast.ai/videos/?lesson=3
Lesson 4https://course.fast.ai/videos/?lesson=4
Lesson 5https://course.fast.ai/videos/?lesson=5
Lesson 6https://course.fast.ai/videos/?lesson=6
Lesson 7https://course.fast.ai/videos/?lesson=7
Lesson 8https://course.fast.ai/videos/?lesson=8
Google: Launching into Machine Learninghttps://www.coursera.org/learn/launching-machine-learning
Book: Grokking Deep Learninghttps://www.manning.com/books/grokking-deep-learning
Book: Make Your Own Neural Networkhttps://www.amazon.com/Make-Your-Own-Neural-Network-ebook/dp/B01EER4Z4G
MIT: 6.S191: Introduction to Deep Learninghttp://introtodeeplearning.com/#schedule
MIT Introduction to Deep Learning | 6.S191https://www.youtube.com/watch?v=njKP3FqW3Sk
Recurrent Neural Networks | MIT 6.S191https://www.youtube.com/watch?v=SEnXr6v2ifU
Convolutional Neural Networks | MIT 6.S191https://www.youtube.com/watch?v=iaSUYvmCekI
Deep Generative Modeling | MIT 6.S191https://www.youtube.com/watch?v=rZufA635dq4
Reinforcement Learning | MIT 6.S191https://www.youtube.com/watch?v=nZfaHIxDD5w
Deep Learning New Frontiers | MIT 6.S191https://www.youtube.com/watch?v=tfM_DdbGTLs
Neurosymbolic AI | MIT 6.S191https://www.youtube.com/watch?v=4PuuziOgSU4
Generalizable Autonomy for Robot Manipulation | MIT 6.S191https://www.youtube.com/watch?v=8Kn4Gi8iSYQ
Neural Rendering | MIT 6.S191https://www.youtube.com/watch?v=BCZ56MU-KhQ
Machine Learning for Scent | MIT 6.S191https://www.youtube.com/watch?v=Z5Pw5eWItiw
Pluralsight: Understanding Algorithms for Recommendation Systemshttps://www.pluralsight.com/courses/algorithms-recommendation-systems
Pluralsight: Deep Learning: The Big Picturehttps://www.pluralsight.com/courses/deep-learning-big-picture
StatQuest: Machine Learninghttps://www.youtube.com/playlist?list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF
A Gentle Introduction to Machine Learninghttps://www.youtube.com/watch?v=Gv9_4yMHFhI
Machine Learning Fundamentals: Cross Validationhttps://www.youtube.com/watch?v=fSytzGwwBVw
Machine Learning Fundamentals: The Confusion Matrixhttps://www.youtube.com/watch?v=Kdsp6soqA7o
Machine Learning Fundamentals: Sensitivity and Specificityhttps://www.youtube.com/watch?v=vP06aMoz4v8
Machine Learning Fundamentals: Bias and Variancehttps://www.youtube.com/watch?v=EuBBz3bI-aA
ROC and AUC, Clearly Explained!https://www.youtube.com/watch?v=4jRBRDbJemM
StatQuest: Fitting a line to data, aka least squares, aka linear regression.https://www.youtube.com/watch?v=PaFPbb66DxQ
StatQuest: Linear Models Pt.1 - Linear Regressionhttps://www.youtube.com/watch?v=nk2CQITm_eo
StatQuest: Odds and Log(Odds), Clearly Explained!!!https://www.youtube.com/watch?v=ARfXDSkQf1Y
StatQuest: Odds Ratios and Log(Odds Ratios), Clearly Explained!!!https://www.youtube.com/watch?v=8nm0G-1uJzA
StatQuest: Logistic Regressionhttps://www.youtube.com/watch?v=yIYKR4sgzI8
Logistic Regression Details Pt1: Coefficientshttps://www.youtube.com/watch?v=vN5cNN2-HWE
Logistic Regression Details Pt 2: Maximum Likelihoodhttps://www.youtube.com/watch?v=BfKanl1aSG0
Logistic Regression Details Pt 3: R-squared and p-valuehttps://www.youtube.com/watch?v=xxFYro8QuXA
Saturated Models and Deviancehttps://www.youtube.com/watch?v=9T0wlKdew6I
Deviance Residualshttps://www.youtube.com/watch?v=JC56jS2gVUE
Regularization Part 1: Ridge (L2) Regressionhttps://www.youtube.com/watch?v=Q81RR3yKn30
Regularization Part 2: Lasso (L1) Regressionhttps://www.youtube.com/watch?v=NGf0voTMlcs
Ridge vs Lasso Regression, Visualized!!!https://www.youtube.com/watch?v=Xm2C_gTAl8c
Regularization Part 3: Elastic Net Regressionhttps://www.youtube.com/watch?v=1dKRdX9bfIo
StatQuest: Principal Component Analysis (PCA), Step-by-Stephttps://www.youtube.com/watch?v=FgakZw6K1QQ
StatQuest: PCA main ideas in only 5 minutes!!!https://www.youtube.com/watch?v=HMOI_lkzW08
StatQuest: PCA - Practical Tipshttps://www.youtube.com/watch?v=oRvgq966yZg
StatQuest: PCA in Pythonhttps://www.youtube.com/watch?v=Lsue2gEM9D0
StatQuest: Linear Discriminant Analysis (LDA) clearly explained.https://www.youtube.com/watch?v=azXCzI57Yfc
StatQuest: MDS and PCoAhttps://www.youtube.com/watch?v=GEn-_dAyYME
StatQuest: t-SNE, Clearly Explainedhttps://www.youtube.com/watch?v=NEaUSP4YerM
StatQuest: Hierarchical Clusteringhttps://www.youtube.com/watch?v=7xHsRkOdVwo
StatQuest: K-means clusteringhttps://www.youtube.com/watch?v=4b5d3muPQmA
StatQuest: K-nearest neighbors, Clearly Explainedhttps://www.youtube.com/watch?v=HVXime0nQeI
Naive Bayes, Clearly Explained!!!https://www.youtube.com/watch?v=O2L2Uv9pdDA
Gaussian Naive Bayes, Clearly Explained!!!https://www.youtube.com/watch?v=H3EjCKtlVog
StatQuest: Decision Treeshttps://www.youtube.com/watch?v=7VeUPuFGJHk
StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Datahttps://www.youtube.com/watch?v=wpNl-JwwplA
Regression Trees, Clearly Explained!!!https://www.youtube.com/watch?v=g9c66TUylZ4
How to Prune Regression Trees, Clearly Explained!!!https://www.youtube.com/watch?v=D0efHEJsfHo
StatQuest: Random Forests Part 1 - Building, Using and Evaluatinghttps://www.youtube.com/watch?v=J4Wdy0Wc_xQ
StatQuest: Random Forests Part 2: Missing data and clusteringhttps://www.youtube.com/watch?v=sQ870aTKqiM
The Chain Rulehttps://www.youtube.com/watch?v=wl1myxrtQHQ
Gradient Descent, Step-by-Stephttps://www.youtube.com/watch?v=sDv4f4s2SB8
Stochastic Gradient Descent, Clearly Explained!!!https://www.youtube.com/watch?v=vMh0zPT0tLI
AdaBoost, Clearly Explainedhttps://www.youtube.com/watch?v=LsK-xG1cLYA
Gradient Boost Part 1: Regression Main Ideashttps://www.youtube.com/watch?v=3CC4N4z3GJc
Gradient Boost Part 2: Regression Detailshttps://www.youtube.com/watch?v=2xudPOBz-vs
Gradient Boost Part 3: Classificationhttps://www.youtube.com/watch?v=jxuNLH5dXCs
Gradient Boost Part 4: Classification Detailshttps://www.youtube.com/watch?v=StWY5QWMXCw
Bam!!! Clearly Explained!!!https://www.youtube.com/watch?v=i4iUvjsGCMc
Support Vector Machines, Clearly Explained!!!https://www.youtube.com/watch?v=efR1C6CvhmE
Support Vector Machines Part 2: The Polynomial Kernelhttps://www.youtube.com/watch?v=Toet3EiSFcM
Support Vector Machines Part 3: The Radial (RBF) Kernelhttps://www.youtube.com/watch?v=Qc5IyLW_hns
XGBoost Part 1: Regressionhttps://www.youtube.com/watch?v=OtD8wVaFm6E
XGBoost Part 2: Classificationhttps://www.youtube.com/watch?v=8b1JEDvenQU
XGBoost Part 3: Mathematical Detailshttps://www.youtube.com/watch?v=ZVFeW798-2I
XGBoost Part 4: Crazy Cool Optimizationshttps://www.youtube.com/watch?v=oRrKeUCEbq8
StatQuest: Fiitting a curve to data, aka lowess, aka loesshttps://www.youtube.com/watch?v=Vf7oJ6z2LCc
Statistics Fundamentals: Population Parametershttps://www.youtube.com/watch?v=vikkiwjQqfU
Principal Component Analysis (PCA) clearly explained (2015)https://www.youtube.com/watch?v=_UVHneBUBW0
Decision Trees in Python from Start to Finishhttps://www.youtube.com/watch?v=q90UDEgYqeI
Udacity: A Friendly Introduction to Machine Learninghttps://www.youtube.com/playlist?list=PLAwxTw4SYaPknYBrOQx6UCyq67kprqXe3
Udacity: Intro to Data Analysishttps://www.udacity.com/course/intro-to-data-analysis--ud170
Udacity: Intro to Data Sciencehttps://www.udacity.com/course/intro-to-data-science--ud359
Udacity: Intro to Machine Learninghttps://www.udacity.com/course/intro-to-machine-learning--ud120
Udacity: Reinforcement Learninghttps://www.udacity.com/course/reinforcement-learning--ud600
Udacity: Deep Learninghttps://www.udacity.com/course/deep-learning--ud730
Udacity: Intro to Artificial Intelligencehttps://www.udacity.com/course/intro-to-artificial-intelligence--cs271
Udacity: Classification Modelshttps://www.udacity.com/course/classification-models--ud978
Youtube: DETR: End-to-End Object Detection with Transformers (Paper Explained)https://www.youtube.com/watch?v=T35ba_VXkMY
Youtube: Sebastian Ruder: Neural Semi-supervised Learning under Domain Shifthttps://www.youtube.com/watch?v=tpAr5-Y4JxU
Youtube: How do we check if a neural network has learned a specific phenomenon?https://www.youtube.com/watch?v=fL22NAtMNYo
Youtube: What is Adversarial Machine Learning and what to do about it? – Adversarial example compilationhttps://youtu.be/YyTyWGUUhmo
Youtube: AI fabricates music in a celebrity's voice (JukeboxAI)https://www.youtube.com/watch?v=7IEEKvcudrA
Youtube: Activation Functions - EXPLAINED!https://www.youtube.com/watch?v=s-V7gKrsels
Youtube: Batch Normalization - EXPLAINED!https://www.youtube.com/watch?v=DtEq44FTPM4
Youtube: Optimizers - EXPLAINED!https://www.youtube.com/watch?v=mdKjMPmcWjY
Youtube: Loss Functions - EXPLAINED!https://www.youtube.com/watch?v=QBbC3Cjsnjg
Youtube: Boosting - EXPLAINED!https://www.youtube.com/watch?v=MIPkK5ZAsms
Youtube: Gradient Descent - THE MATH YOU SHOULD KNOWhttps://www.youtube.com/watch?v=-p1ldISb90Q
Youtube: Logistic Regression - VISUALIZED!https://www.youtube.com/watch?v=slBI5YuVUTM
Youtube: Linear Regression and Multiple Regressionhttps://www.youtube.com/watch?v=K_EH2abOp00
Youtube: Precision, Recall & F-Measurehttps://www.youtube.com/watch?v=j-EB6RqqjGI
Youtube: Bootstrapping, Bagging and Random Forestshttps://www.youtube.com/watch?v=3R0AW-vrPEw
Youtube: Deep Mind's AlphaGo Zero - EXPLAINEDhttps://www.youtube.com/watch?v=NJBLx29JuHs
Youtube: Curiosity in AIhttps://www.youtube.com/watch?v=xPCCyiw8M2U
Youtube: DropBlock - A BETTER DROPOUT for Neural Networkshttps://www.youtube.com/watch?v=GcvGxXePI2g
Youtube: Neural Voice Cloninghttps://www.youtube.com/watch?v=gVehTbi6Ipc
Youtube: Neural Networks from Scratch in Pythonhttps://www.youtube.com/playlist?list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3
Neural Networks from Scratch - P.1 Intro and Neuron Codehttps://www.youtube.com/watch?v=Wo5dMEP_BbI
Neural Networks from Scratch - P.2 Coding a Layerhttps://www.youtube.com/watch?v=lGLto9Xd7bU
Neural Networks from Scratch - P.3 The Dot Producthttps://www.youtube.com/watch?v=tMrbN67U9d4
Neural Networks from Scratch - P.4 Batches, Layers, and Objectshttps://www.youtube.com/watch?v=TEWy9vZcxW4
Neural Networks from Scratch - P.5 Hidden Layer Activation Functionshttps://www.youtube.com/watch?v=gmjzbpSVY1A
Youtube: Visualizing Deep Learninghttps://www.youtube.com/playlist?list=PLyPKqVSnetmEOp_g_hfabuRAs9ET-shl_
The Neural Network, A Visual Introduction | Visualizing Deep Learning, Chapter 1https://youtu.be/UOvPeC8WOt8?list=PLyPKqVSnetmEOp_g_hfabuRAs9ET-shl_
Youtube: Deep Double Descenthttps://youtu.be/R29awq6jvUw
https://github.com/StudyWithJeffrey/learning#be-able-to-implement-models-in-scikit-learn
Datacamp: Supervised Learning with scikit-learnhttps://www.datacamp.com/courses/supervised-learning-with-scikit-learn
Datacamp: Machine Learning with Tree-Based Models in Pythonhttps://www.datacamp.com/courses/machine-learning-with-tree-based-models-in-python
Datacamp: Introduction to Linear Modeling in Pythonhttps://www.datacamp.com/courses/introduction-to-linear-modeling-in-python
Datacamp: Linear Classifiers in Pythonhttps://www.datacamp.com/courses/linear-classifiers-in-python
Datacamp: Generalized Linear Models in Pythonhttps://www.datacamp.com/courses/generalized-linear-models-in-python
Pluralsight: Building Machine Learning Models in Python with scikit-learnhttps://www.pluralsight.com/courses/python-scikit-learn-building-machine-learning-models
Youtube: Applied Machine Learning 2020https://www.youtube.com/playlist?list=PL_pVmAaAnxIRnSw6wiCpSvshFyCREZmlM
Channel Intro - Applied Machine Learninghttps://www.youtube.com/watch?v=d79mzijMAw0
Applied ML 2020 - 01 Introductionhttps://www.youtube.com/watch?v=rbvpiPJuK64
Applied ML 2020 - 02 Visualization and matplotlibhttps://www.youtube.com/watch?v=OW3oco7nlV4
Applied ML 2020 - 03 Supervised learning and model validationhttps://www.youtube.com/watch?v=7_YzyMYC2zM
Applied ML 2020 - 04 - Preprocessinghttps://www.youtube.com/watch?v=XpOBSaktb6s
Applied ML 2020 - 05 - Linear Models for Regressionhttps://www.youtube.com/watch?v=-OOsfj5Revo
Applied ML 2020 - 06 - Linear Models for Classificationhttps://www.youtube.com/watch?v=_dqBhUrq09U
Applied ML 2020 - 07 - Decision Trees and Random Forestshttps://www.youtube.com/watch?v=nomd5ylZ2dw
Applied ML 2020 - 08 - Gradient Boostinghttps://www.youtube.com/watch?v=yrTW5YTmFjw
Applied ML 2020 - 09 - Model Evaluation and Metricshttps://www.youtube.com/watch?v=trg3YkCsjqE
Applied ML 2020 - 10 - Calibration, Imbalanced datahttps://www.youtube.com/watch?v=w3OPq0V8fr8
Applied ML 2020 - 11 - Model Inspection and Feature Selectionhttps://www.youtube.com/watch?v=FDhyS6Xjxa8
Applied ML 2020 - 12 - AutoML (plus some feature selection)https://www.youtube.com/watch?v=bmBezdqHTAg
Applied ML 2020 - 13 - Dimensionality reductionhttps://www.youtube.com/watch?v=CrFOGyU32PM
Applied ML 2020 - 14 - Clustering and Mixture Modelshttps://www.youtube.com/watch?v=HFioJ62H7dM
Applied ML 2020 - 15 - Working with Text Datahttps://www.youtube.com/watch?v=A8yDjNsUQJA
Applied ML 2020 - 16 - Topic models for text datahttps://www.youtube.com/watch?v=xdmFx4-3Ukw
Applied ML 2020 - 17 - Word vectors and document embeddingshttps://www.youtube.com/watch?v=xgjnlGBpLUs
Applied ML 2020 - 18 - Neural Networkshttps://www.youtube.com/watch?v=CRRPLlgYWZw
Applied ML 2020 - 19 - Keras and Convolutional neural netshttps://www.youtube.com/watch?v=PP7Hr3tGbIo
Applied ML 2020 - 20 - Advanced neural networkshttps://www.youtube.com/watch?v=2FNmbX901r0
Applied ML 2020 - 21 - Time Series and Forecastinghttps://www.youtube.com/watch?v=GVGEnaJsuu8
https://github.com/StudyWithJeffrey/learning#be-able-to-implement-models-in-tensorflow-and-keras
Coursera: Introduction to Tensorflowhttps://www.coursera.org/learn/introduction-tensorflow
Coursera: Convolutional Neural Networks in TensorFlowhttps://www.coursera.org/learn/convolutional-neural-networks-tensorflow
Coursera: Getting Started With Tensorflow 2https://www.coursera.org/learn/getting-started-with-tensor-flow2
Coursera: Customising your models with TensorFlow 2https://www.coursera.org/learn/customising-models-tensorflow2
Deeplizard: Keras - Python Deep Learning Neural Network APIhttps://www.youtube.com/playlist?list=PLZbbT5o_s2xrwRnXk_yCPtnqqo4_u2YGL
Book: Deep Learning with Python (Page: 276)https://www.manning.com/books/deep-learning-with-python
Datacamp: Deep Learning in Pythonhttps://www.datacamp.com/courses/deep-learning-in-python
Datacamp: Convolutional Neural Networks for Image Processinghttps://www.datacamp.com/courses/convolutional-neural-networks-for-image-processing
Datacamp: Introduction to TensorFlow in Pythonhttps://www.datacamp.com/courses/introduction-to-tensorflow-in-python
Datacamp: Introduction to Deep Learning with Kerashttps://www.datacamp.com/courses/deep-learning-with-keras-in-python
Datacamp: Advanced Deep Learning with Kerashttps://www.datacamp.com/courses/advanced-deep-learning-with-keras-in-python
Google: Intro to Tensorflowhttps://www.coursera.org/learn/intro-tensorflow
Google: Machine Learning Crash Coursehttps://developers.google.com/machine-learning/crash-course/
Pluralsight: Deep Learning with Kerashttps://www.pluralsight.com/courses/keras-deep-learning
Udacity: Intro to TensorFlow for Deep Learninghttps://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187
https://github.com/StudyWithJeffrey/learning#be-able-to-implement-models-in-pytorch
Article: An introduction to PyTorch Lightning with comparisons to PyTorchhttps://amaarora.github.io/2020/07/12/oganized-pytorch.html
Datacamp: Introduction to Deep Learning with PyTorchhttps://www.datacamp.com/courses/deep-learning-with-pytorch
Deeplizard: Neural Network Programming - Deep Learning with PyTorchhttps://www.youtube.com/playlist?list=PLZbbT5o_s2xrfNyHZsM6ufI0iZENK9xgG
Udacity: Intro to Deep Learning with PyTorchhttps://www.udacity.com/course/deep-learning-pytorch--ud188
Youtube: PyTorch Lightning 101https://www.youtube.com/playlist?list=PLaMu-SDt_RB5NUm67hU2pdE75j6KaIOv2
Training a classification model on MNIST with PyTorchhttps://youtu.be/OMDn66kM9Qc?list=PLaMu-SDt_RB5NUm67hU2pdE75j6KaIOv2
From PyTorch to PyTorch Lightninghttps://youtu.be/DbESHcCoWbM?list=PLaMu-SDt_RB5NUm67hU2pdE75j6KaIOv2
Lightning Data Moduleshttps://youtu.be/L---MBeSXFw
PyTorch Dropout, Batch size and interactive debugginghttps://youtu.be/vD5iQkdqMqU
Youtube: SimCLR with PyTorch Lightninghttps://www.youtube.com/playlist?list=PLaMu-SDt_RB4k8VXiB3hOdsn0Y3GoXo1k
Youtube: PyTorch Performance Tuning Guidehttps://youtu.be/9mS1fIYj1So
Youtube: Skin Cancer Detection with PyTorchhttps://www.youtube.com/playlist?list=PLUH_l3HbfEW0wP7ZOKUxmlnG6sntCQpHX
[PART 1] Skin Cancer Detection with PyTorchhttps://www.youtube.com/watch?v=6LdS9n_L7u4
[PART 2] Skin Cancer Detection with PyTorchhttps://www.youtube.com/watch?v=wspdT8hRCWs
[PART 3] Skin Cancer Detection with PyTorchhttps://www.youtube.com/watch?v=zFqsuXs6-Us
https://github.com/StudyWithJeffrey/learning#be-able-to-apply-unsupervised-learning-algorithms
Article: Grouping data points with k-means clusteringhttps://www.jeremyjordan.me/grouping-data-points-with-k-means-clustering/
Article: Soft clustering with Gaussian mixed models (EM)https://www.jeremyjordan.me/gaussian-mixed-models/
Article: Introduction to autoencodershttps://www.jeremyjordan.me/autoencoders/
Article: Variational autoencodershttps://www.jeremyjordan.me/variational-autoencoders/
Article: Principal components analysis (PCA)https://www.jeremyjordan.me/principal-components-analysis/
Article: Deep Inside Autoencodershttps://nathanhubens.github.io/posts/deep%20learning/2018/02/25/deep-inside-autoencoders.html
Article: Build a simple Image Retrieval System with an Autoencoderhttps://nathanhubens.github.io/posts/deep%20learning/2018/08/24/image-retrieval.html
Article: Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentshttps://medium.com/@nainaakash012/unsupervised-learning-of-visual-features-by-contrasting-cluster-assignments-fbedc8b9c3db
Article: A Framework For Contrastive Self-Supervised Learning And Designing A New Approachhttps://towardsdatascience.com/a-framework-for-contrastive-self-supervised-learning-and-designing-a-new-approach-3caab5d29619
Article: Understanding self-supervised and contrastive learning with "Bootstrap Your Own Latent" (BYOL)https://untitled-ai.github.io/understanding-self-supervised-contrastive-learning.html
Article: Affinity Propagation Algorithm Explainedhttps://towardsdatascience.com/unsupervised-machine-learning-affinity-propagation-algorithm-explained-d1fef85f22c8
Article: Algorithm Breakdown: Affinity Propagationhttps://www.ritchievink.com/blog/2018/05/18/algorithm-breakdown-affinity-propagation/
Article: From Autoencoder to Beta-VAEhttps://lilianweng.github.io/lil-log/2018/08/12/from-autoencoder-to-beta-vae.html
Article: Self-Supervised Representation Learninghttps://lilianweng.github.io/lil-log/2019/11/10/self-supervised-learning.html
Article: GANs in computer vision - Introduction to generative learninghttps://theaisummer.com/gan-computer-vision/
Article: GANs in computer vision - self-supervised adversarial training and high-resolution image synthesis with style incorporationhttps://theaisummer.com/gan-computer-vision-style-gan/
Article: GANs in computer vision - semantic image synthesis and learning a generative model from a single imagehttps://theaisummer.com/gan-computer-vision-semantic-synthesis/
Article: GANs in computer vision - Improved training with Wasserstein distance, game theory control and progressively growing schemeshttps://theaisummer.com/gan-computer-vision-incremental-training/
Article: GANs in computer vision - Conditional image synthesis and 3D object generationhttps://theaisummer.com/gan-computer-vision-object-generation/
Article: Decrypt Generative Adversarial Networks (GAN)https://theaisummer.com/Generative_Artificial_Intelligence/
Article: How to Generate Images using Autoencodershttps://theaisummer.com/Autoencoder/
Article: Deepfakes: Face synthesis with GANs and Autoencodershttps://theaisummer.com/deepfakes/
Berkeley: Deep Unsupervised Learning Spring 2020https://www.youtube.com/playlist?list=PLwRJQ4m4UJjPiJP3691u-qWwPGVKzSlNP
L1 Introduction -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley, Spring 2020https://www.youtube.com/watch?v=V9Roouqfu-M
L2 Autoregressive Models -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley, Spring 2020https://www.youtube.com/watch?v=iyEOk8KCRUw
L3 Flow Models -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley -- Spring 2020https://www.youtube.com/watch?v=JBb5sSC0JoY
L4 Latent Variable Models (VAE) -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeleyhttps://www.youtube.com/watch?v=FMuvUZXMzKM
Lecture 5 Implicit Models -- GANs Part I --- UC Berkeley, Spring 2020https://www.youtube.com/watch?v=1CT-kxjYbFU
Lecture 6 Implicit Models / GANs part II --- CS294-158-SP20 Deep Unsupervised Learning -- Berkeleyhttps://www.youtube.com/watch?v=0W1dixJfKL4
Lecture 7 Self-Supervised Learning -- UC Berkeley Spring 2020 - CS294-158 Deep Unsupervised Learninghttps://www.youtube.com/watch?v=dMUes74-nYY
L8 Round-up of Strengths and Weaknesses of Unsupervised Learning Methods -- UC Berkeley SP20https://www.youtube.com/watch?v=1sJuWg5dULg
L9 Semi-Supervised Learning and Unsupervised Distribution Alignment -- CS294-158-SP20 UC Berkeleyhttps://www.youtube.com/watch?v=PXOhi6m09bA
L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learninghttps://www.youtube.com/watch?v=pPyOlGvWoXA
L11 Language Models -- guest instructor: Alec Radford (OpenAI) --- Deep Unsupervised Learning SP20https://www.youtube.com/watch?v=BnpB3GrpsfM
L12 Representation Learning for Reinforcement Learning --- CS294-158 UC Berkeley Spring 2020https://www.youtube.com/watch?v=YqvhDPd1UEw
Datacamp: Customer Segmentation in Pythonhttps://www.datacamp.com/courses/customer-segmentation-in-python
Datacamp: Unsupervised Learning in Pythonhttps://www.datacamp.com/courses/unsupervised-learning-in-python
Google: Clusteringhttps://developers.google.com/machine-learning/clustering
Google: Recommendation Systemshttps://developers.google.com/machine-learning/recommendation
Udacity: Segmentation and Clusteringhttps://www.udacity.com/course/segmentation-and-clustering--ud981
Youtube: BYOL: Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (Paper Explained)https://www.youtube.com/watch?v=YPfUiOMYOEE&feature=youtu.be
Youtube: A critical analysis of self-supervision, or what we can learn from a single image (Paper Explained)https://youtu.be/l5he9JNJqHA
Youtube: Week 10 – Lecture: Self-supervised learning (SSL) in computer vision (CV)https://www.youtube.com/watch?v=0KeR6i1_56g&feature=youtu.be
Youtube: CVPR 2020 Tutorial: Towards Annotation-Efficient Learninghttps://youtu.be/MaGudzppu3I
Youtube: Yuki Asano | Self-Supervision | Self-Labelling | Labelling Unlabelled videos | CV | CTDS.Show #81https://youtu.be/LPdbnasJ9wI
Youtube: Contrastive Clustering with SwAVhttps://youtu.be/jCg97EAVsy8
Youtube: Variational Autoencoders - EXPLAINED!https://www.youtube.com/watch?v=fcvYpzHmhvA
Youtube: OptaProAnalyticsForum– Learning to watch football: Self-supervised representations for tracking datahttps://youtu.be/H1iho17lnoI
Youtube: Can a Neural Net tell if an image is mirrored? – Visual Chiralityhttps://youtu.be/rbg1Mdo2LZM
Youtube: Deep InfoMax: Learning deep representations by mutual information estimation and maximizationhttps://www.youtube.com/watch?v=o1HIkn8LEsw
Deep Learning Lecture Summer 2020https://www.youtube.com/playlist?list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj
Deep Learning: Unsupervised Learning - Part 1https://www.youtube.com/watch?v=aoOE4bJxybA&list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj&index=47&t=0s
Deep Learning: Unsupervised Learning - Part 2https://www.youtube.com/watch?v=GpAHm7dvP_k&list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj&index=48&t=0s
Deep Learning: Unsupervised Learning - Part 3https://www.youtube.com/watch?v=fXO1fOXnOTI&list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj&index=49&t=0s
Deep Learning: Unsupervised Learning - Part 4https://www.youtube.com/watch?v=K27a_doRoxw&list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj&index=50&t=0s
Deep Learning: Unsupervised Learning - Part 5https://www.youtube.com/watch?v=4Ot22wkEdfU&list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj&index=51&t=0s
Deep Learning: Weakly and Self-Supervised Learning - Part 1https://www.youtube.com/watch?v=Vj_JeSZG1EA&list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj&index=57&t=0s
Deep Learning: Weakly and Self-Supervised Learning - Part 2https://www.youtube.com/watch?v=KjcSfpLin7U&list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj&index=58&t=0s
Deep Learning: Weakly and Self-Supervised Learning - Part 3https://www.youtube.com/watch?v=EqMwbP7Smxg&list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj&index=59&t=0s
Deep Learning: Weakly and Self-Supervised Learning - Part 4https://www.youtube.com/watch?v=zIDdTstAqWU&list=PLpOGQvPCDQzvgpD3S0vTy7bJe2pf_yJFj&index=60&t=0s
ECCV 2020: New Frontiers for Learning with Limited Labels or Datahttps://nvlabs.github.io/eccv2020-limited-labels-data-tutorial/
Introduction to New Frontiers on Learning with Limited Labels or Datahttps://youtu.be/lHsLUYk80z4
Self-Supervised Part and Viewpoint Discovery from Image Collectionshttps://youtu.be/5kzU6NkvGX4
Learning Visual Correspondences across Instances and Video Frameshttps://youtu.be/_Sug0ICzKlk
Limitless Labels in a Labelless World: Weak Supervision with Noisy Labelshttps://youtu.be/UtxQkIoei0o
Inverting Neural Networks for Data-free Knowledge Transferhttps://youtu.be/ddEtea4ntEU
Learning Efficiently with Biologically Inspired Feedbackhttps://youtu.be/8N9AF8V52-E
Youtube: Self-Supervised Learning - What is Next? - Workshop at ECCV 2020, August 28thhttps://www.youtube.com/playlist?list=PL53R9Jy9Cc0zdv9OqvJ5YsZH2-AMKo9gM
Next Challenges for Self-Supervised Learning - Aäron van den Oordhttps://www.youtube.com/watch?v=jJozjCG8Cqs
Perspectives on Unsupervised Representation Learning - Paolo Favarohttps://www.youtube.com/watch?v=APwHDZZcLuY
Learning and Transferring Visual Representations with Few Labels - Carl Doerschhttps://www.youtube.com/watch?v=RWCc0nZOSBw
Multi-view Invariance and Grouping for Self-Supervised Learning - Ishan Misrahttps://www.youtube.com/watch?v=gbziPIn9uDI
Representation Learning beyond Instance Discrimination and Semantic Categorization - Stella Yuhttps://www.youtube.com/watch?v=F5mt4z-w_Mk
Self-Supervision as a Path to a Post-Dataset Era - Alexei Alyosha Efroshttps://www.youtube.com/watch?v=iTbfEXFwDJc
Self-Supervision & Modularity: Cornerstones for Generalization in Embodied Agents - Deepak Pathakhttps://www.youtube.com/watch?v=fUMpC_hoedA
https://github.com/StudyWithJeffrey/learning#be-able-to-implement-computer-vision-models
Article: What is Focal Loss and when should you use it?https://amaarora.github.io/2020/06/29/FocalLoss.html
Article: Squeeze and Excitation Networks Explained with PyTorch Implementationhttps://amaarora.github.io/2020/07/24/SeNet.html
Article: DenseNet Architecture Explained with PyTorch Implementation from TorchVisionhttps://amaarora.github.io/2020/08/02/densenets.html
Article: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkshttps://amaarora.github.io/2020/08/13/efficientnet.html
Article: Group Normalizationhttps://amaarora.github.io/2020/08/09/groupnorm.html
Article: A Short Introduction to Generative Adversarial Networkshttps://sthalles.github.io/semi-supervised-learning-with-gans/
Article: Semi-supervised Learning with GANshttps://sthalles.github.io/intro-to-gans/
Article: Densely Connected Convolutional Networks in Tensorflowhttps://sthalles.github.io/densely-connected-conv-nets/
Article: Convolutional neural networkshttps://www.jeremyjordan.me/convolutional-neural-networks/
Article: Common architectures in convolutional neural networkshttps://www.jeremyjordan.me/convnet-architectures/
Article: An overview of semantic image segmentationhttps://www.jeremyjordan.me/semantic-segmentation/
Article: Evaluating image segmentation modelshttps://www.jeremyjordan.me/evaluating-image-segmentation-models/
Article: An overview of object detection: one-stage methodshttps://www.jeremyjordan.me/object-detection-one-stage/
Article: A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNNhttps://blog.athelas.com/a-brief-history-of-cnns-in-image-segmentation-from-r-cnn-to-mask-r-cnn-34ea83205de4
Article: Object Detection for Dummies Part 1: Gradient Vector, HOG, and SShttps://lilianweng.github.io/lil-log/2017/10/29/object-recognition-for-dummies-part-1.html
Article: Object Detection for Dummies Part 2: CNN, DPM and Overfeathttps://lilianweng.github.io/lil-log/2017/12/15/object-recognition-for-dummies-part-2.html
Article: Object Detection for Dummies Part 3: R-CNN Familyhttps://lilianweng.github.io/lil-log/2017/12/31/object-recognition-for-dummies-part-3.html
Article: Understanding coordinate systems and DICOM for deep learning medical image analysishttps://theaisummer.com/medical-image-coordinates/
Article: Understanding the receptive field of deep convolutional networkshttps://theaisummer.com/receptive-field/
Article: Deep learning in medical imaging - 3D medical image segmentation with PyTorchhttps://theaisummer.com/medical-image-deep-learning/
Article: Intuitive Explanation of Skip Connections in Deep Learninghttps://theaisummer.com/skip-connections/
Article: Human Pose Estimationhttps://theaisummer.com/Human-Pose-Estimation/
Article: YOLO - You only look once (Single shot detectors)https://theaisummer.com/YOLO/
Article: Localization and Object Detection with Deep Learninghttps://theaisummer.com/Localization_and_Object_Detection/
Article: Semantic Segmentation in the era of Neural Networkshttps://theaisummer.com/Semantic_Segmentation/
Article: ECCV 2020: Some Highlightshttps://yassouali.github.io/ml-blog/eccv2020/
Book: Deep Learning for Computer Vision with Pythonhttps://www.pyimagesearch.com/deep-learning-computer-vision-python-book/
Book: Practical Python and OpenCVhttps://www.pyimagesearch.com/practical-python-opencv/
Coursera: Convolutional Neural Networkshttps://www.coursera.org/learn/convolutional-neural-networks?specialization=deep-learning
Datacamp: Biomedical Image Analysis in Pythonhttps://www.datacamp.com/courses/biomedical-image-analysis-in-python
Datacamp: Image Processing in Pythonhttps://www.datacamp.com/courses/image-processing-in-python
Google: ML Practicum: Image Classificationhttps://developers.google.com/machine-learning/practica/image-classification
Stanford: CS231N Winter 2016https://www.youtube.com/playlist?list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC
CS231n Winter 2016: Lecture 1: Introduction and Historical Contexthttps://www.youtube.com/watch?v=NfnWJUyUJYU
CS231n Winter 2016: Lecture 2: Data-driven approach, kNN, Linear Classification 1https://www.youtube.com/watch?v=8inugqHkfvE
CS231n Winter 2016: Lecture 3: Linear Classification 2, Optimizationhttps://www.youtube.com/watch?v=qlLChbHhbg4
CS231n Winter 2016: Lecture 4: Backpropagation, Neural Networks 1https://www.youtube.com/watch?v=i94OvYb6noo
CS231n Winter 2016: Lecture 5: Neural Networks Part 2https://www.youtube.com/watch?v=gYpoJMlgyXA
CS231n Winter 2016: Lecture 6: Neural Networks Part 3 / Intro to ConvNetshttps://www.youtube.com/watch?v=hd_KFJ5ktUc
CS231n Winter 2016: Lecture 7: Convolutional Neural Networkshttps://www.youtube.com/watch?v=LxfUGhug-iQ
CS231n Winter 2016: Lecture 8: Localization and Detectionhttps://www.youtube.com/watch?v=GxZrEKZfW2o
CS231n Winter 2016: Lecture 9: Visualization, Deep Dream, Neural Style, Adversarial Exampleshttps://www.youtube.com/watch?v=ta5fdaqDT3M
CS231n Winter 2016: Lecture 10: Recurrent Neural Networks, Image Captioning, LSTMhttps://www.youtube.com/watch?v=yCC09vCHzF8
CS231n Winter 2016: Lecture 11: ConvNets in practicehttps://www.youtube.com/watch?v=pA4BsUK3oP4
CS231n Winter 2016: Lecture 12: Deep Learning librarieshttps://www.youtube.com/watch?v=Vf_-OkqbwPo
CS231n Winter 2016: Lecture 14: Videos and Unsupervised Learninghttps://www.youtube.com/watch?v=ekyBklxwQMU
CS231n Winter 2016: Lecture 13: Segmentation, soft attention, spatial transformershttps://www.youtube.com/watch?v=ByjaPdWXKJ4
CS231n Winter 2016: Lecture 15: Invited Talk by Jeff Deanhttps://www.youtube.com/watch?v=T7YkPWpwFD4
Udacity: Introduction to Computer Visionhttps://www.udacity.com/course/introduction-to-computer-vision--ud810
Youtube: Deep Residual Learning for Image Recognition (Paper Explained)https://www.youtube.com/watch?v=GWt6Fu05voI
Youtube: Implementing ResNet from scratchhttps://www.youtube.com/playlist?list=PLbMqOoYQ3MxywF4R6MOJO7i9jFEeSGSSC
Youtube: ConvNets Scaled Efficientlyhttps://www.youtube.com/watch?v=fC39F8AqPo0
Youtube: Building an Image Captioner with Neural Networkshttps://www.youtube.com/watch?v=c_bVBYxX5EU
Youtube: Evolution of Face Generation | Evolution of GANshttps://www.youtube.com/watch?v=C1YUYWP-6rE
Youtube: Autoencoders - EXPLAINEDhttps://www.youtube.com/watch?v=7mRfwaGGAPg
Youtube: Unpaired Image-Image Translation using CycleGANshttps://www.youtube.com/watch?v=NyAosnNQv_U
Youtube: AI creates Image Classifiers…by DRAWING?https://www.youtube.com/watch?v=BeYbQkbKox8
Youtube: The Evolution of Convolution Neural Networkshttps://www.youtube.com/watch?v=Y2Tna77k1aI
Youtube: Depthwise Separable Convolution - A FASTER CONVOLUTION!https://www.youtube.com/watch?v=T7o3xvJLuHk
Youtube: Mask Region based Convolution Neural Networks - EXPLAINED!https://www.youtube.com/watch?v=4tkgOzQ9yyo
Youtube: Sound play with Convolution Neural Networkshttps://www.youtube.com/watch?v=GNza2ncnMfA
Youtube: Convolution Neural Networks - EXPLAINEDhttps://www.youtube.com/watch?v=m8pOnJxOcqY
Youtube: Generative Adversarial Networks - FUTURISTIC & FUN AI !https://www.youtube.com/watch?v=O8LAi6ksC80
https://github.com/StudyWithJeffrey/learning#be-able-to-implement-nlp-models
Article: The Annotated GPT-2https://amaarora.github.io/2020/02/18/annotatedGPT2.html
Article: Introduction to recurrent neural networkshttps://www.jeremyjordan.me/introduction-to-recurrent-neural-networks/
Article: Aspect-Based Opinion Mining (NLP with Python)https://medium.com/@pmin91/aspect-based-opinion-mining-nlp-with-python-a53eb4752800
Article: The Transformer Explainedhttps://nostalgebraist.tumblr.com/post/185326092369/the-transformer-explained
Article: Controlling Text Generation with Plug and Play Language Modelshttps://eng.uber.com/pplm/
Article: What makes a good conversation?http://www.abigailsee.com/2019/08/13/what-makes-a-good-conversation.html
Article: NLP for Supervised Learning - A Brief Surveyhttps://eugeneyan.com/writing/nlp-supervised-learning-survey/
Article: Generating Questions Using Transformershttps://amontgomerie.github.io/2020/07/30/question-generator.html
Article: Neural Language Models as Domain-Specific Knowledge Baseshttps://www.statestitle.com/resource/neural-language-models-as-domain-specific-knowledge-bases/
Article: Understanding BERT’s Semantic Interpretationshttps://www.statestitle.com/resource/understanding-berts-semantic-interpretations/
Article: Using NLP (BERT) to improve OCR accuracyhttps://www.statestitle.com/resource/using-nlp-bert-to-improve-ocr-accuracy/
Article: Hyperparameter Optimization for 🤗Transformers: A guidehttps://medium.com/distributed-computing-with-ray/hyperparameter-optimization-for-transformers-a-guide-c4e32c6c989b
Article: Faster and smaller quantized NLP with Hugging Face and ONNX Runtimehttps://medium.com/microsoftazure/faster-and-smaller-quantized-nlp-with-hugging-face-and-onnx-runtime-ec5525473bb7
Article: Learning Word Embeddinghttps://lilianweng.github.io/lil-log/2017/10/15/learning-word-embedding.html
Article: The Transformer Familyhttps://lilianweng.github.io/lil-log/2020/04/07/the-transformer-family.html
Article: Generalized Language Modelshttps://lilianweng.github.io/lil-log/2019/01/31/generalized-language-models.html
Article: Document clusteringhttps://theaisummer.com/Document_clustering/
Article: The Unreasonable Effectiveness of Recurrent Neural Networkshttp://karpathy.github.io/2015/05/21/rnn-effectiveness/
Article: LSTM Primer With Real Life Application( DeepMind Kidney Injury Prediction )*https://medium.com/@ranko.mosic/lstm-primer-6d7e1cfa704a
Article: Making sense of LSTMs by examplehttps://alexander-schiendorfer.github.io/2020/02/08/making-sense-of-lstms.html
Article: 3 subword algorithms help to improve your NLP model performancehttps://medium.com/@makcedward/how-subword-helps-on-your-nlp-model-83dd1b836f46
Article: Exploring LSTMshttp://blog.echen.me/2017/05/30/exploring-lstms/
Article: Understanding LSTM Networkshttp://colah.github.io/posts/2015-08-Understanding-LSTMs/
Article: 74 Summaries of Machine Learning and NLP Researchhttp://www.marekrei.com/blog/74-summaries-of-machine-learning-and-nlp-research/
A friendly introduction to Recurrent Neural Networkshttps://www.youtube.com/watch?v=UNmqTiOnRfg
Coursera: Sequence Modelshttps://www.coursera.org/learn/nlp-sequence-models
Coursera: Natural Language Processing in TensorFlowhttps://www.coursera.org/learn/natural-language-processing-tensorflow
CMU: Low-resource NLP Bootcamp 2020https://www.youtube.com/playlist?list=PL8PYTP1V4I8A1CpCzURXAUa6H4HO7PF2c
CMU Low resource NLP Bootcamp 2020 (1): NLP Taskshttps://www.youtube.com/watch?v=glIbcpay1-I
CMU Low resource NLP Bootcamp 2020 (2): Linguistics - Phonology and Morphologyhttps://www.youtube.com/watch?v=KGOYGONxypA
CMU Low resource NLP Bootcamp 2020 (3): Machine Translationhttps://www.youtube.com/watch?v=SIZfkGzyVRc
CMU Low resource NLP Bootcamp 2020 (4): Linguistics - Syntax and Morphosyntaxhttps://www.youtube.com/watch?v=j2vd3bTfrIA
CMU Low resource NLP Bootcamp 2020 (5): Neural Representation Learninghttps://www.youtube.com/watch?v=FgYg1ZH5Io8
CMU Low resource NLP Bootcamp 2020 (6): Multilingual NLPhttps://www.youtube.com/watch?v=wWE4db9XgHA
CMU Low resource NLP Bootcamp 2020 (7): Speech Synthesishttps://www.youtube.com/watch?v=eDjtEsOvouM
CMU Low resource NLP Bootcamp 2020 (8): Speech Recognitionhttps://www.youtube.com/watch?v=XDnUHu6PAqA
CMU: Neural Nets for NLP 2020https://www.youtube.com/playlist?list=PL8PYTP1V4I8CJ7nMxMC8aXv8WqKYwj-aJ
CMU Neural Nets for NLP 2020 (1): Introductionhttps://www.youtube.com/watch?v=D7o2Z1tAuQc
CMU Neural Nets for NLP 2020 (2): Language Modeling, Efficiency/Training Trickshttps://www.youtube.com/watch?v=aTxfVIzyN4o
CMU Neural Nets for NLP 2020 (3): Convolutional Neural Networks for Texthttps://www.youtube.com/watch?v=UirRyzNq3nM
CMU Neural Nets for NLP 2020 (4): Recurrent Neural Networkshttps://www.youtube.com/watch?v=wD-mB2clN_0
CMU Neural Nets for NLP 2020 (5): Efficiency Tricks for Neural Netshttps://www.youtube.com/watch?v=eokkF3qv8_U
CMU Neural Nets for NLP 2020 (7): Attentionhttps://www.youtube.com/watch?v=jDaJYOmF2iQ
CMU Neural Nets for NLP 2020 (8): Distributional Semantics and Word Vectorshttps://www.youtube.com/watch?v=RRaU7pz2eT4
CMU Neural Nets for NLP 2020 (9): Sentence and Contextual Word Representationshttps://www.youtube.com/watch?v=EjoTMiZPVC8
CMU Neural Nets for NLP 2020 (10): Debugging Neural Nets (for NLP)https://www.youtube.com/watch?v=-I-3qRg3ExI
CMU Neural Nets for NLP 2020 (11): Structured Prediction with Local Independence Assumptionshttps://www.youtube.com/watch?v=ry-__6gNSqE
CMU Neural Nets for NLP 2020 (12): Generating Trees Incrementallyhttps://www.youtube.com/watch?v=-bG-QfVrsYw
CMU Neural Nets for NLP 2020 (13): Generating Trees Incrementallyhttps://www.youtube.com/watch?v=8f_IzoafNgc
CMU Neural Nets for NLP 2020 (14): Search-based Structured Predictionhttps://www.youtube.com/watch?v=9OA8IybwI00
CMU Neural Nets for NLP 2020 (15): Minimum Risk Training and Reinforcement Learninghttps://www.youtube.com/watch?v=W_x7BL-8VZc
CMU Neural Nets for NLP 2020 (16): Advanced Search Algorithmshttps://www.youtube.com/watch?v=mfOCPBOHVjY
CMU Neural Nets for NLP 2020 (17): Adversarial Methodshttps://www.youtube.com/watch?v=4SjdBB64mjo
CMU Neural Nets for NLP 2020 (18): Models w/ Latent Random Variableshttps://www.youtube.com/watch?v=5OL1_YECHvM
CMU Neural Nets for NLP 2020 (19): Unsupervised and Semi-supervised Learning of Structurehttps://www.youtube.com/watch?v=rpAzfgr3OGc
CMU Neural Nets for NLP 2020 (20): Multitask and Multilingual Learninghttps://www.youtube.com/watch?v=6_gMeW_cunQ
CMU Neural Nets for NLP 2020 (21): Document Level Modelshttps://www.youtube.com/watch?v=K72U5dlPwkY
CMU Neural Nets for NLP 2020 (22): Neural Nets + Knowledge Baseshttps://www.youtube.com/watch?v=Lcb5YKE21P8
CMU Neural Nets for NLP 2020 (23): Machine Reading w/ Neural Netshttps://www.youtube.com/watch?v=cGHVNwgVLRY
CMU Neural Nets for NLP 2020 (24): Natural Language Generationhttps://www.youtube.com/watch?v=dyXTVhDCwCQ
CMU Neural Nets for NLP 2020 (25): Model Interpretationhttps://www.youtube.com/watch?v=ePEJqqj7Y8M
CMU Multilingual NLP 2020http://demo.clab.cs.cmu.edu/11737fa20/
CMU Multilingual NLP (1): Introductionhttps://www.youtube.com/watch?v=xeu7LKIT194
CMU Multilingual NLP (2): Typology - The Space of Languagehttps://www.youtube.com/watch?v=4QilRTLxvCc
Datacamp: Advanced NLP with spaCyhttps://www.datacamp.com/courses/advanced-nlp-with-spacy
Datacamp: Building Chatbots in Pythonhttps://www.datacamp.com/courses/building-chatbots-in-python
Datacamp: Clustering Methods with SciPyhttps://www.datacamp.com/courses/clustering-methods-with-scipy
Datacamp: Feature Engineering for NLP in Pythonhttps://www.datacamp.com/courses/feature-engineering-for-nlp-in-python
Datacamp: Machine Translation in Pythonhttps://www.datacamp.com/courses/machine-translation-in-python
Datacamp: Natural Language Processing Fundamentals in Pythonhttps://www.datacamp.com/courses/natural-language-processing-fundamentals-in-python
Datacamp: Natural Language Generation in Pythonhttps://www.datacamp.com/courses/natural-language-generation-in-python
Datacamp: RNN for Language Modelinghttps://www.datacamp.com/courses/recurrent-neural-networks-for-language-modeling-in-python
Datacamp: Regular Expressions in Pythonhttps://www.datacamp.com/courses/regular-expressions-in-python
Datacamp: Sentiment Analysis in Pythonhttps://www.datacamp.com/courses/sentiment-analysis-in-python
Datacamp: Spoken Language Processing in Pythonhttps://www.datacamp.com/courses/spoken-language-processing-in-python
RNN and LSTMhttps://www.youtube.com/watch?v=WCUNPb-5EYI&index=2&list=PLVZqlMpoM6kbaeySxhdtgQPFEC5nV7Faa&t=0s
Spacy Tutorialhttps://www.youtube.com/watch?v=cgwDB1THUBY&list=PLJ39kWiJXSiz1LK8d_fyxb7FTn4mBYOsD
Stanford CS224U: Natural Language Understanding | Spring 2019https://www.youtube.com/playlist?list=PLoROMvodv4rObpMCir6rNNUlFAn56Js20
Lecture 1 – Course Overview | Stanford CS224U: Natural Language Understanding | Spring 2019https://www.youtube.com/watch?v=tZ_Jrc_nRJY
Lecture 2 – Word Vectors 1 | Stanford CS224U: Natural Language Understanding | Spring 2019https://www.youtube.com/watch?v=IYMYI9AJpQs
Lecture 3 – Word Vectors 2 | Stanford CS224U: Natural Language Understanding | Spring 2019https://www.youtube.com/watch?v=nH4rn3X8i0c
Lecture 4 – Word Vectors 3 | Stanford CS224U: Natural Language Understanding | Spring 2019https://www.youtube.com/watch?v=pip8h9vjTHY
Lecture 5 – Sentiment Analysis 1 | Stanford CS224U: Natural Language Understanding | Spring 2019https://www.youtube.com/watch?v=O1Xh3H1uEYY
Lecture 6 – Sentiment Analysis 2 | Stanford CS224U: Natural Language Understanding | Spring 2019https://www.youtube.com/watch?v=6-4pJt1M18s
Lecture 7 – Relation Extraction | Stanford CS224U: Natural Language Understanding | Spring 2019https://www.youtube.com/watch?v=pO3Jsr31s_Q
Lecture 8 – NLI 1 | Stanford CS224U: Natural Language Understanding | Spring 2019https://www.youtube.com/watch?v=M_VPUF9ResU
Lecture 9 – NLI 2 | Stanford CS224U: Natural Language Understanding | Spring 2019https://www.youtube.com/watch?v=JXtH_ABQFX0
Lecture 10 – Grounding | Stanford CS224U: Natural Language Understanding | Spring 2019https://www.youtube.com/watch?v=7b2_3dDTKMc
Lecture 11 – Semantic Parsing | Stanford CS224U: Natural Language Understanding | Spring 2019https://www.youtube.com/watch?v=C5bdflsg7rs
Lecture 12 – Evaluation Methods | Stanford CS224U: Natural Language Understanding | Spring 2019https://www.youtube.com/watch?v=3UGti9Ju5j8
Lecture 13 – Evaluation Metrics | Stanford CS224U: Natural Language Understanding | Spring 2019https://www.youtube.com/watch?v=YygGzfkhtJc
Lecture 14 – Contextual Vectors | Stanford CS224U: Natural Language Understanding | Spring 2019https://www.youtube.com/watch?v=lzBB7xoZ3Q8
Lecture 15 – Presenting Your Work | Stanford CS224U: Natural Language Understanding | Spring 2019https://www.youtube.com/watch?v=WXLb4h2A724
Stanford CS224N: Stanford CS224N: NLP with Deep Learning | Winter 2019https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 1 – Introduction and Word Vectorshttps://www.youtube.com/watch?v=8rXD5-xhemo
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 2 – Word Vectors and Word Senseshttps://www.youtube.com/watch?v=kEMJRjEdNzM
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 3 – Neural Networkshttps://www.youtube.com/watch?v=8CWyBNX6eDo
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 4 – Backpropagationhttps://www.youtube.com/watch?v=yLYHDSv-288
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 5 – Dependency Parsinghttps://www.youtube.com/watch?v=nC9_RfjYwqA
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 6 – Language Models and RNNshttps://www.youtube.com/watch?v=iWea12EAu6U
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 7 – Vanishing Gradients, Fancy RNNshttps://www.youtube.com/watch?v=QEw0qEa0E50
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 8 – Translation, Seq2Seq, Attentionhttps://www.youtube.com/watch?v=XXtpJxZBa2c
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 9 – Practical Tips for Projectshttps://www.youtube.com/watch?v=fyqm8fRDgl0
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 10 – Question Answeringhttps://www.youtube.com/watch?v=yIdF-17HwSk
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 11 – Convolutional Networks for NLPhttps://www.youtube.com/watch?v=EAJoRA0KX7I
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 12 – Subword Modelshttps://www.youtube.com/watch?v=9oTHFx0Gg3Q
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 13 – Contextual Word Embeddingshttps://www.youtube.com/watch?v=S-CspeZ8FHc
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 14 – Transformers and Self-Attentionhttps://www.youtube.com/watch?v=5vcj8kSwBCY
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 15 – Natural Language Generationhttps://www.youtube.com/watch?v=4uG1NMKNWCU
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 16 – Coreference Resolutionhttps://www.youtube.com/watch?v=i19m4GzBhfc
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 17 – Multitask Learninghttps://www.youtube.com/watch?v=M8dsZsEtEsg
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 18 – Constituency Parsing, TreeRNNshttps://www.youtube.com/watch?v=6Z4A3RSf-HY
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 19 – Bias in AIhttps://www.youtube.com/watch?v=XR8YSRcuVLE
Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 20 – Future of NLP + Deep Learninghttps://www.youtube.com/watch?v=3wWZBGN-iX8
TextBlob Tutorial Serieshttps://www.youtube.com/watch?v=4k2cqUIjb8g&list=PLJ39kWiJXSizrWpC7hcu1_mLNxEPzN0gF
Natural Language Processing Tutorial With TextBlob -Tokens,Translation and Ngramshttps://www.youtube.com/watch?v=4k2cqUIjb8g
NLP Tutorial With TextBlob and Python - Parts of Speech Tagginghttps://www.youtube.com/watch?v=aWhqoPLr6Jg
NLP Tutorial With TextBlob & Python - Lemmatizatinghttps://www.youtube.com/watch?v=tNUoqSlzM_k
NLP Tutorial with TextBlob & Python - Sentiment Analysis(Polarity,Subjectivity)https://www.youtube.com/watch?v=dR9Dcq-qgIE
Building a NLP-based Flask App with TextBlobhttps://www.youtube.com/watch?v=7tLBHkqMae8
Natural Language Processing with Polyglot - Installation & Introhttps://www.youtube.com/watch?v=qtMEp6WxwCQ
Treehouse: Regular expressionhttps://teamtreehouse.com/library/regular-expressions-in-python
Youtube: fast.ai Code-First Intro to Natural Language Processinghttps://www.youtube.com/playlist?list=PLtmWHNX-gukKocXQOkQjuVxglSDYWsSh9
What is NLP? (NLP video 1)https://www.youtube.com/watch?v=cce8ntxP_XI
Topic Modeling with SVD & NMF (NLP video 2)https://www.youtube.com/watch?v=tG3pUwmGjsc
Topic Modeling & SVD revisited (NLP video 3)https://www.youtube.com/watch?v=lRZ4aMaXPBI
Sentiment Classification with Naive Bayes (NLP video 4)https://www.youtube.com/watch?v=hp2ipC5pW4I
Sentiment Classification with Naive Bayes & Logistic Regression, contd. (NLP video 5)https://www.youtube.com/watch?v=dt7sArnLo1g
Derivation of Naive Bayes & Numerical Stability (NLP video 6)https://www.youtube.com/watch?v=z8-Tbrg1-rE
Revisiting Naive Bayes, and Regex (NLP video 7)https://www.youtube.com/watch?v=Q1zLqfnEXdw
Intro to Language Modeling (NLP video 8)https://www.youtube.com/watch?v=PNNHaQUQqW8
Transfer learning (NLP video 9)https://www.youtube.com/watch?v=5gCQvuznKn0
ULMFit for non-English Languages (NLP Video 10)https://www.youtube.com/watch?v=MDX_x6rKXAs
Understanding RNNs (NLP video 11)https://www.youtube.com/watch?v=l1rlFh0PmZw
Seq2Seq Translation (NLP video 12)https://www.youtube.com/watch?v=IfsjMg4fLWQ
Word embeddings quantify 100 years of gender & ethnic stereotypes-- Nikhil Garg (NLP video 13)https://www.youtube.com/watch?v=boxV8Od4jqQ
Text generation algorithms (NLP video 14)https://www.youtube.com/watch?v=3oEb_fFmPnY
Implementing a GRU (NLP video 15)https://www.youtube.com/watch?v=Bl6WVj6wQaE
Algorithmic Bias (NLP video 16)https://www.youtube.com/watch?v=pThqge9QDn8
Introduction to the Transformer (NLP video 17)https://www.youtube.com/watch?v=AFkGPmU16QA
The Transformer for language translation (NLP video 18)https://www.youtube.com/watch?v=KzfyftiH7R8
What you need to know about Disinformation (NLP video 19)https://www.youtube.com/watch?v=vbva2RN-rbQ
Article: Zero to Hero with fastai - Beginnerhttps://muellerzr.github.io/fastblog/2020/08/20/_08_21-beginner.html
Article: Zero to Hero with fastai - Intermediatehttps://muellerzr.github.io/fastblog/2020/08/20/_08_21-intermediate.html
NLP Course | For Youhttps://lena-voita.github.io/nlp_course.html
Word Embeddingshttps://lena-voita.github.io/nlp_course/word_embeddings.html
Text Classificationhttps://github.com/StudyWithJeffrey/learning
Language Modelinghttps://github.com/StudyWithJeffrey/learning
Seq2seq and Attentionhttps://github.com/StudyWithJeffrey/learning
Youtube: BERT Research Serieshttps://www.youtube.com/playlist?list=PLam9sigHPGwOBuH4_4fr-XvDbe5uneaf6
YouTube: Intro to NLP with Spacyhttps://www.youtube.com/playlist?list=PLBmcuObd5An559HbDr_alBnwVsGq-7uTF
Talk: Practical NLP for the Real Worldhttps://www.infoq.com/presentations/practical-nlp/
YouTube: Level 3 AI Assistant Conference 2020https://www.youtube.com/playlist?list=PL75e0qA87dlGP51yZ0dyNup-vwu0Rlv86
Youtube: Conversation Analysis Theory in Chatbots | Michael Szulhttps://youtu.be/osQChzuhUiU?list=PL75e0qA87dlGP51yZ0dyNup-vwu0Rlv86
Youtube: Designing Practical NLP Solutions | Ines Montanihttps://youtu.be/JpkzK58lkmA?list=PL75e0qA87dlGP51yZ0dyNup-vwu0Rlv86
Youtube: Effective Copywriting for Chatbots | Hans Van Damhttps://youtu.be/49G58PQWO7w?list=LLqn7Nv8Zg6tWbBonrUOJGwQ
Youtube: Distilling BERT | Sam Sucikhttps://youtu.be/Xji8NmL3FvQ?list=LLqn7Nv8Zg6tWbBonrUOJGwQ
Youtube: Transformer Policies that improve Dialogues: A Live Demo by Vincent Warmerdamhttps://youtu.be/P5SUS3V50zQ?list=LLqn7Nv8Zg6tWbBonrUOJGwQ
Youtube: From Research to Production – Our Process at Rasa | Tanja Bunkhttps://youtu.be/5_lRfLFjfEs?list=LLqn7Nv8Zg6tWbBonrUOJGwQ
Youtube: Keynote: Perspective on the 5 Levels of Conversational AI | Alan Nicholhttps://youtu.be/bAkToyQhWyo?list=LLqn7Nv8Zg6tWbBonrUOJGwQ
Youtube: RASA Algorithm Whiteboardhttps://www.youtube.com/playlist?list=PL75e0qA87dlG-za8eLI6t0_Pbxafk-cxb
Introducing The Algorithm Whiteboardhttps://www.youtube.com/watch?v=wWNMST6t1TA
Rasa Algorithm Whiteboard - Diet Architecture 1: How it Workshttps://www.youtube.com/watch?v=vWStcJDuOUk
Rasa Algorithm Whiteboard - Diet Architecture 2: Design Decisionshttps://www.youtube.com/watch?v=KUGGuJ0aTL8
Rasa Algorithm Whiteboard - Diet Architecture 3: Benchmarkinghttps://www.youtube.com/watch?v=oj5oPGDlep4
Rasa Algorithm Whiteboard - Embeddings 1: Just Lettershttps://www.youtube.com/watch?v=mWvnlVw_LiY
Rasa Algorithm Whiteboard - Embeddings 2: CBOW and Skip Gramhttps://www.youtube.com/watch?v=BWaHLmG1lak
Rasa Algorithm Whiteboard - Embeddings 3: GloVehttps://www.youtube.com/watch?v=QoUYlxl1RGI
Rasa Algorithm Whiteboard - Embeddings 4: Whatlieshttps://www.youtube.com/watch?v=FwkwC7IJWO0
Rasa Algorithm Whiteboard - Attention 1: Self Attentionhttps://www.youtube.com/watch?v=yGTUuEx3GkA
Rasa Algorithm Whiteboard - Attention 2: Keys, Values, Querieshttps://www.youtube.com/watch?v=tIvKXrEDMhk
Rasa Algorithm Whiteboard - Attention 3: Multi Head Attentionhttps://www.youtube.com/watch?v=23XUv0T9L5c
Rasa Algorithm Whiteboard: Attention 4 - Transformershttps://www.youtube.com/watch?v=EXNBy8G43MM
Rasa Algorithm Whiteboard - StarSpacehttps://www.youtube.com/watch?v=ZT3_9Kjx7oI
Rasa Algorithm Whiteboard - TED Policyhttps://www.youtube.com/watch?v=j90NvurJI4I
Rasa Algorithm Whiteboard - TED in Practicehttps://www.youtube.com/watch?v=d8JMJMvErSg
Rasa Algorithm Whiteboard - Response Selectionhttps://www.youtube.com/watch?v=2jvyWngHEJM
Rasa Algorithm Whiteboard - Response Selection: Implementationhttps://www.youtube.com/watch?v=0tXkFScW0hE
Rasa Algorithm Whiteboard - Countvectorshttps://www.youtube.com/watch?v=Ju7l5ADg10U
Rasa Algorithm Whiteboard - Subword Embeddingshttps://www.youtube.com/watch?v=kNw9dpzp5RU
Rasa Algorithm Whiteboard - Implementation of Subword Embeddingshttps://www.youtube.com/watch?v=8D3Gamk1Jig
Rasa Algorithm Whiteboard - BytePair Embeddingshttps://www.youtube.com/watch?v=-0IjF-7OB3s
Youtube: A brief history of the Transformer architecture in NLPhttps://www.youtube.com/watch?v=iH-wmtxHunk
Youtube: The Transformer neural network architecture explained. “Attention is all you need” (NLP)https://www.youtube.com/watch?v=FWFA4DGuzSc
Youtube: How does a Transformer architecture combine Vision and Language? ViLBERT - NLP meets Computer Visionhttps://www.youtube.com/watch?v=dd7nE4nbxN0
Youtube: Strategies for pre-training the BERT-based Transformer architecture – language (and vision)https://www.youtube.com/watch?v=dabFOBE4eZI
Youtube: Ilya Sutskever - GPT-2https://youtu.be/T0I88NhR_9M
Youtube: NLP Masterclass | Modeling Fallacies in NLPhttps://youtu.be/f2m6Mon0VE8?t=223
Youtube: What is GPT-3? Showcase, possibilities, and implicationshttps://youtu.be/5fqxPOaaqi0
Youtube: TextAttack: A Framework for Data Augmentation and Adversarial Training in NLPhttps://youtu.be/VpLAjOQHaLU?list=LLqn7Nv8Zg6tWbBonrUOJGwQ
Youtube: Learning to Rank: From Theory to Production - Malvina Josephidou & Diego Ceccarelli, Bloomberghttps://youtu.be/eMuepJpjUjI
Youtube: Learning "Learning to Rank"https://youtu.be/7teudGhdnqo
Youtube: Learning to rank search results - Byron Voorbach & Jettro Coenradie [DevCon 2018]https://youtu.be/TG7aNLgzIcM
Article: How the Embedding Layers in BERT Were Implementedhttps://medium.com%2F@medium.com/@_init_/why-bert-has-3-embedding-layers-and-their-implementation-details-9c261108e28a
Youtube: Easy Data Augmentation for Text Classificationhttps://www.youtube.com/watch?v=3w92peJtYNQ&feature=youtu.be
Youtube: Webinar: Special NLP Session with Hugging Facehttps://www.youtube.com/watch?v=SUqi_E_Lyjs
Youtube: BERT Neural Network - EXPLAINED!https://www.youtube.com/watch?v=xI0HHN5XKDo
Youtube: NLP with Neural Networks & Transformershttps://www.youtube.com/watch?v=BGKumht1qLA
Youtube: Transformer Neural Networks - EXPLAINED! (Attention is all you need)https://www.youtube.com/watch?v=TQQlZhbC5ps
Youtube: LSTM Networks - EXPLAINED!https://www.youtube.com/watch?v=QciIcRxJvsM
Youtube: Recurrent Neural Networks - EXPLAINED!https://www.youtube.com/watch?v=yZv_yRgOvMg
Youtube: Attention in Neural Networkshttps://www.youtube.com/watch?v=W2rWgXJBZhU
Youtube: Spacy IRL 2019https://www.youtube.com/playlist?list=PLBmcuObd5An4UC6jvK_-eSl6jCvP1gwXc
Sebastian Ruder: Transfer Learning in Open-Source Natural Language Processing (spaCy IRL 2019)https://www.youtube.com/watch?v=hNPwRPg9BrQ
Giannis Daras: Improving sparse transformer models for efficient self-attention (spaCy IRL 2019)https://www.youtube.com/watch?v=KwKr_e7xBQ4
Peter Baumgartner: Applied NLP: Lessons from the Field (spaCy IRL 2019)https://www.youtube.com/watch?v=QRGMJWwOU94
Justina Petraitytė: Lessons learned in helping ship conversational AI assistants (spaCy IRL 2019)https://www.youtube.com/watch?v=1jI0mTcNRUU
Yoav Goldberg: The missing elements in NLP (spaCy IRL 2019)https://www.youtube.com/watch?v=e12danHhlic
Sofie Van Landeghem: Entity linking functionality in spaCy (spaCy IRL 2019)https://www.youtube.com/watch?v=PW3RJM8tDGo
Guadalupe Romero: Rethinking rule-based lemmatization (spaCy IRL 2019)https://www.youtube.com/watch?v=88zcQODyuko
Mark Neumann: ScispaCy: A spaCy pipeline & models for scientific & biomedical text (spaCy IRL 2019)https://www.youtube.com/watch?v=2_HSKDALwuw
Patrick Harrison: Financial NLP at S&P Global (spaCy IRL 2019)https://www.youtube.com/watch?v=rdmaR4WRYEM
McKenzie Marshall: NLP in Asset Management (spaCy IRL 2019)https://www.youtube.com/watch?v=kX14Ycieju8
David Dodson: spaCy in the News: Quartz's NLP pipeline (spaCy IRL 2019)https://www.youtube.com/watch?v=azrVX8JksMU
Matthew Honnibal & Ines Montani: spaCy and Explosion: past, present & future (spaCy IRL 2019)https://www.youtube.com/watch?v=Jk9y17lvltY
Youtube: The Future of Natural Language Processinghttps://youtu.be/G5lmya6eKtc
Youtube: Sentiment Analysis: Key Milestones, Challenges and New Directionshttps://www.youtube.com/watch?v=YAqjf7to-lU
Youtube: Simple and Efficient Deep Learning for Natural Language Processing, with Moshe Wasserblat, Intel AIhttps://www.youtube.com/watch?v=Bgr684dPJ6U
Youtube: Why not solve biological problems with a Transformer? BERTology meets Biologyhttps://www.youtube.com/watch?v=pFf4PltQ9LY
Youtube: Introduction to NLPhttps://www.youtube.com/playlist?list=PLM8wYQRetTxCCURc1zaoxo9pTsoov3ipY
Introduction to NLP | Bag of Words Modelhttps://www.youtube.com/watch?v=8Mlc4-3tgzc
Introduction to NLP | TF-IDFhttps://www.youtube.com/watch?v=aOIHiclLDrc
Introduction to NLP | Text Cleaning and Preprocessinghttps://www.youtube.com/watch?v=p6yvuST_6oQ
Introduction to NLP | Word Embeddings & Word2Vec Modelhttps://www.youtube.com/watch?v=_Rt4LjasO34
Introduction to NLP | GloVe Model Explainedhttps://www.youtube.com/watch?v=Fn_U2OG1uqI
Introduction to NLP | GloVe & Word2Vec Transfer Learninghttps://www.youtube.com/watch?v=oMd7sMlxYFk
Introduction to NLP | How to Train Custom Word Vectorshttps://www.youtube.com/watch?v=-Y_tldJX9jk
Sarcasm is Very Easy to Detect! GloVe + LSTMhttps://www.youtube.com/watch?v=pMjT8GIX0co
Text Summarization & Keyword Extraction | Introduction to NLPhttps://www.youtube.com/watch?v=XO97Uon83Os
Youtube: Self-attention step-by-step | How to get meaning from texthttps://youtu.be/-9vVhYEXeyQ
Youtube: Chat Bot with PyTorchhttps://www.youtube.com/playlist?list=PLqnslRFeH2UrFW4AUgn-eY37qOAWQpJyg
Youtube: NLP with Friends Talkshttps://www.youtube.com/playlist?list=PL0zsOCvKa2iEqmPV6WGhjuP-tsrUy102C
NLP with Friends, Featured Friend: Tom McCoyhttps://www.youtube.com/watch?v=2w1jZyLHzsc
NLP with Friends, Featured Friend: Maarten Saphttps://www.youtube.com/watch?v=1bSk00tEpaM
NLP with Friends, featured friend: Nitika Mathurhttps://www.youtube.com/watch?v=w4zyfZV5Q8I
NLP with Friends, Featured Friend: Sabrina J Mielkehttps://www.youtube.com/watch?v=4-ulM2moEWg
Youtube: Insincere Question Classification with PyTorchhttps://www.youtube.com/playlist?list=PLUH_l3HbfEW3Nyst9FTbPiRosViWQRZDL&app=desktop
[PART 1] Insincere Question Classification with PyTorchhttps://www.youtube.com/watch?v=mmUttpXu7oE
[PART 2] Insincere Question Classification with PyTorchhttps://www.youtube.com/watch?v=m3JGRoNSdqE
[PART 3] Insincere Question Classification with PyTorchhttps://www.youtube.com/watch?v=-bfYCBznJ6s
[PART 4] Insincere Question Classification with PyTorchhttps://www.youtube.com/watch?v=e9CxGeQZTVA
Crash Course: Linguisticshttps://www.youtube.com/playlist?list=PL8dPuuaLjXtP5mp25nStsuDzk2blncJDW
Crash Course Linguistics Previewhttps://www.youtube.com/watch?v=eDop3FDoUzk
What is Linguistics?: Crash Course Linguistics #1https://www.youtube.com/watch?v=3yLXNzDUH58
https://github.com/StudyWithJeffrey/learning#be-able-to-model-graphs-and-network-data
Datacamp: Network Analysis in Python (Part 1)https://www.datacamp.com/courses/network-analysis-in-python-part-1
Datacamp: Network Analysis in Python (Part 2)https://www.datacamp.com/courses/network-analysis-in-python-part-2
https://github.com/StudyWithJeffrey/learning#be-able-to-implement-models-for-timeseries-and-forecasting
Datacamp: Machine Learning for Finance in Pythonhttps://www.datacamp.com/courses/machine-learning-for-finance-in-python
Datacamp: Introduction to Time Series Analysis in Pythonhttps://www.datacamp.com/courses/introduction-to-time-series-analysis-in-python
Datacamp: Machine Learning for Time Series Data in Pythonhttps://www.datacamp.com/courses/machine-learning-for-time-series-data-in-python
Datacamp: Intro to Portfolio Risk Management in Pythonhttps://www.datacamp.com/courses/intro-to-portfolio-risk-management-in-python
Datacamp: Financial Forecasting in Pythonhttps://www.datacamp.com/courses/financial-forecasting-in-python
Datacamp: Predicting CTR with Machine Learning in Pythonhttps://www.datacamp.com/courses/predicting-ctr-with-machine-learning-in-python
Datacamp: Intro to Financial Concepts using Pythonhttps://www.datacamp.com/courses/intro-to-financial-concepts-using-python
Datacamp: Fraud Detection in Pythonhttps://www.datacamp.com/courses/fraud-detection-in-python
Datacamp: Forecasting Using ARIMA Models in Pythonhttps://www.datacamp.com/courses/forecasting-using-arima-models-in-python
Datacamp: Introduction to Portfolio Analysis in Pythonhttps://www.datacamp.com/courses/introduction-to-portfolio-analysis-in-python
Datacamp: Credit Risk Modeling in Pythonhttps://www.datacamp.com/courses/credit-risk-modeling-in-python
Datacamp: Machine Learning for Marketing in Pythonhttps://www.datacamp.com/courses/machine-learning-for-marketing-in-python
Udacity: Machine Learning for Tradinghttps://www.udacity.com/course/machine-learning-for-trading--ud501
Udacity: Time Series Forecastinghttps://www.udacity.com/course/time-series-forecasting--ud980
https://github.com/StudyWithJeffrey/learning#be-familiar-with-reinforcement-learning
DeepLizard: Reinforcement Learning - Goal Oriented Intelligencehttps://www.youtube.com/playlist?list=PLZbbT5o_s2xoWNVdDudn51XM8lOuZ_Njv
Reinforcement Learning Series Intro - Syllabus Overviewhttps://www.youtube.com/watch?v=nyjbcRQ-uQ8
Markov Decision Processes (MDPs) - Structuring a Reinforcement Learning Problemhttps://www.youtube.com/watch?v=my207WNoeyA
Expected Return - What Drives a Reinforcement Learning Agent in an MDPhttps://www.youtube.com/watch?v=a-SnJtmBtyA
Policies and Value Functions - Good Actions for a Reinforcement Learning Agenthttps://www.youtube.com/watch?v=eMxOGwbdqKY
What do Reinforcement Learning Algorithms Learn - Optimal Policieshttps://www.youtube.com/watch?v=rP4oEpQbDm4
Q-Learning Explained - A Reinforcement Learning Techniquehttps://www.youtube.com/watch?v=qhRNvCVVJaA
Exploration vs. Exploitation - Learning the Optimal Reinforcement Learning Policyhttps://www.youtube.com/watch?v=mo96Nqlo1L8
OpenAI Gym and Python for Q-learning - Reinforcement Learning Code Projecthttps://www.youtube.com/watch?v=QK_PP_2KgGE
Train Q-learning Agent with Python - Reinforcement Learning Code Projecthttps://www.youtube.com/watch?v=HGeI30uATws
Watch Q-learning Agent Play Game with Python - Reinforcement Learning Code Projecthttps://www.youtube.com/watch?v=ZaILVnqZFCg
Deep Q-Learning - Combining Neural Networks and Reinforcement Learninghttps://www.youtube.com/watch?v=wrBUkpiRvCA
Replay Memory Explained - Experience for Deep Q-Network Traininghttps://www.youtube.com/watch?v=Bcuj2fTH4_4
Training a Deep Q-Network - Reinforcement Learninghttps://www.youtube.com/watch?v=0bt0SjbS3xc
Training a Deep Q-Network with Fixed Q-targets - Reinforcement Learninghttps://www.youtube.com/watch?v=xVkPh9E9GfE
Deep Q-Network Code Project Intro - Reinforcement Learninghttps://www.youtube.com/watch?v=FU-sNVew9ZA
Build Deep Q-Network - Reinforcement Learning Code Projecthttps://www.youtube.com/watch?v=PyQNfsGUnQA
Deep Q-Network Image Processing and Environment Management - Reinforcement Learning Code Projecthttps://www.youtube.com/watch?v=jkdXDinWfo8
Deep Q-Network Training Code - Reinforcement Learning Code Projecthttps://www.youtube.com/watch?v=ewRw996uevM
https://github.com/StudyWithJeffrey/learning#be-able-to-use-managed-ml-services-on-the-cloud
AWS: Amazon Transcribe Deep Dive: Using Feedback Loops to Improve Confidence Level of Transcriptionhttps://www.aws.training/learningobject/video?id=27495
AWS: Build a Text Classification Model with AWS Glue and Amazon SageMakerhttps://www.aws.training/learningobject/video?id=27225
AWS: Deep Dive on Amazon Rekognition: Building Computer Visions Based Smart Applicationshttps://www.aws.training/learningobject/video?id=27230
AWS: Hands-on Rekognition: Automated Video Editinghttps://www.aws.training/learningobject/video?id=27229
AWS: Introduction to Amazon Comprehendhttps://www.aws.training/learningobject/video?id=16626
AWS: Introduction to Amazon Comprehend Medicalhttps://www.aws.training/learningobject/video?id=27159
AWS: Introduction to Amazon Elastic Inferencehttps://www.aws.training/learningobject/video?id=27172
AWS: Introduction to Amazon Forecasthttps://www.aws.training/learningobject/video?id=27163
AWS: Introduction to Amazon Lexhttps://www.aws.training/learningobject/video?id=16516
AWS: Introduction to Amazon Personalizehttps://www.aws.training/learningobject/video?id=27158
AWS: Introduction to Amazon Pollyhttps://www.aws.training/learningobject/video?id=15886
AWS: Introduction to Amazon SageMaker Ground Truthhttps://www.aws.training/learningobject/video?id=27162
AWS: Introduction to Amazon SageMaker Neohttps://www.aws.training/learningobject/video?id=27160
AWS: Introduction to Amazon Transcribehttps://www.aws.training/learningobject/video?id=19443
AWS: Introduction to Amazon Translatehttps://www.aws.training/learningobject/video?id=19442
AWS: Introduction to AWS Marketplace - Machine Learning Categoryhttps://www.aws.training/learningobject/video?id=27165
AWS: Machine Learning Exam Basicshttps://www.aws.training/learningobject/curriculum?id=27271
AWS: Neural Machine Translation with Sockeyehttps://www.aws.training/learningobject/video?id=27236
AWS: Process Model: CRISP-DM on the AWS Stackhttps://www.aws.training/learningobject/wbc?id=27200
AWS: Satellite Image Classification in SageMakerhttps://www.aws.training/learningobject/video?id=27231
edX: Amazon SageMaker: Simplifying Machine Learning Application Developmenthttps://www.edx.org/course/simplifying-machine-learning-app-development-with-amazon-sagemaker
https://github.com/StudyWithJeffrey/learning#be-able-to-optimize-performance-metric
Article: Evaluating a machine learning modelhttps://www.jeremyjordan.me/evaluating-a-machine-learning-model/
Article: Hyperparameter tuning for machine learning modelshttps://www.jeremyjordan.me/hyperparameter-tuning/
Article: Hacker's Guide to Hyperparameter Tuninghttps://www.curiousily.com/posts/hackers-guide-to-hyperparameter-tuning/
Coursera: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimizationhttps://www.coursera.org/learn/deep-neural-network?specialization=deep-learning
Datacamp: Model Validation in Pythonhttps://www.datacamp.com/courses/model-validation-in-python
Datacamp: Hyperparameter Tuning in Pythonhttps://www.datacamp.com/courses/hyperparameter-tuning-in-python
Google: Testing and Debugginghttps://developers.google.com/machine-learning/testing-debugging
Troubleshooting Deep Neural Networkshttp://josh-tobin.com/assets/pdf/troubleshooting-deep-neural-networks-01-19.pdf
Youtube: How do GPUs speed up Neural Network training?https://www.youtube.com/watch?v=EKD1kEMNeeU
Youtube: Why use GPU with Neural Networks?https://www.youtube.com/watch?v=GRRMi7UfZHg
https://github.com/StudyWithJeffrey/learning#be-able-to-optimize-models-for-production
Article: Neural Network Pruninghttps://nathanhubens.github.io/posts/deep%20learning/2020/05/22/pruning.html
Article: FasterAIhttps://nathanhubens.github.io/posts/deep%20learning/2020/08/17/FasterAI.html
Article: Is the future of Neural Networks Sparse? An Introduction (1/N)https://medium.com/huggingface/is-the-future-of-neural-networks-sparse-an-introduction-1-n-d03923ecbd70
Article: Sparse Neural Networks (2/N): Understanding GPU Performance.https://medium.com/huggingface/sparse-neural-networks-2-n-gpu-performance-b8bc9ce950fc
Article: Block Sparse Matrices for Smaller and Faster Language Modelshttps://huggingface.co/blog/pytorch_block_sparse
https://github.com/StudyWithJeffrey/learning#be-able-to-deploy-model-to-production
Acloudguru: AWS Certified Machine Learning - Specialtyhttps://acloud.guru/learn/aws-certified-machine-learning-specialty
Acloudguru: AWS Certified Developer - Associatehttps://acloud.guru/learn/aws-certified-developer-associate-june-2018
Acloudguru: AWS Certification Preparation Guidehttps://acloud.guru/learn/aws-certification-preparation
AWS: Exam Readiness: AWS Certified Developer – Associatehttps://www.aws.training/training/schedule?courseId=18953
AWS: Thirty Serverless Architectures in 30 Minuteshttps://www.youtube.com/watch?v=xJcm9V2jagc
Article: Deploy a Keras Deep Learning Project to Production with Flaskhttps://www.curiousily.com/posts/deploy-keras-deep-learning-project-to-production-with-flask/
Article: Logging and Debugging in Machine Learning - How to use Python debugger and the logging module to find errors in your AI applicationhttps://theaisummer.com/logging-debugging/
Article: How to Unit Test Deep Learning: Tests in TensorFlow, mocking and test coveragehttps://theaisummer.com/unit-test-deep-learning/
Article: Best practices to write Deep Learning code: Project structure, OOP, Type checking and documentationhttps://theaisummer.com/best-practices-deep-learning-code/
Article: Deep Learning in Production: Laptop set up and system designhttps://theaisummer.com/deep-learning-production/
Article: Enough Docker to be Dangeroushttp://seankross.com/2017/09/17/Enough-Docker-to-be-Dangerous.html
Article: How to properly ship and deploy your machine learning modelhttps://towardsdatascience.com/how-to-properly-ship-and-deploy-your-machine-learning-model-8a8664b763c4
Luigi Patruno: ML in Productionhttps://mlinproduction.com/
Video: You trained a machine learning model. Now what?https://www.youtube.com/watch?v=Vugbn17LDPQ
Article: Docker for Machine Learning – Part Ihttps://mlinproduction.com/docker-for-ml-part-1/
Article: Docker for Machine Learning – Part IIhttps://mlinproduction.com/docker-for-ml-part-2/
Article: Docker for Machine Learning – Part IIIhttps://mlinproduction.com/docker-for-ml-part-3/
Article: Using Docker to Generate Machine Learning Predictions in Real Timehttps://mlinproduction.com/docker-for-ml-part-4/
Article: Batch Inference vs Online Inferencehttps://mlinproduction.com/batch-inference-vs-online-inference/
Article: Storing Metadata from Machine Learning Experimentshttps://mlinproduction.com/ml-metadata/
Article: How Data Leakage Impacts Machine Learning Modelshttps://mlinproduction.com/data-leakage/
Article: An Introduction to Kubernetes for Data Scientistshttps://mlinproduction.com/intro-to-kubernetes/
Article: How to Use Kubernetes Pods for Machine Learninghttps://mlinproduction.com/k8s-pods/
Article: Kubernetes Jobs for Machine Learninghttps://mlinproduction.com/k8s-jobs/
Article: Kubernetes CronJobs for Machine Learninghttps://mlinproduction.com/k8s-cronjobs/
Article: Kubernetes Deployments for Machine Learninghttps://mlinproduction.com/k8s-deployments/
Article: Kubernetes Services for Machine Learninghttps://mlinproduction.com/k8s-services/
Article: The Ultimate Guide to Model Retraininghttps://mlinproduction.com/model-retraining/
Article: Top ML Resources: Interview with Eric Colsonhttps://mlinproduction.com/top-30-ml-in-production-resources-guide-eric-colson-interview/
Article: Top ML Resources: Interview with Veronika Megler, PhDhttps://mlinproduction.com/top-30-ml-in-production-resources-guide-veronika-megler-interview/
Article: Top ML Resources: Interview with Erik Bernhardssonhttps://mlinproduction.com/top-30-ml-in-production-resources-guide-erik-bernhardsson-interview/
Article: Top ML Resources: Interview with Rui Carmohttps://mlinproduction.com/top-30-ml-in-production-resources-guide-rui-carmo-interview/
Article: Top ML Resources: Interview with Jeremy Jordanhttps://mlinproduction.com/top-30-ml-in-production-resources-guide-jeremy-jordan-interview/
Article: 5 Challenges to Running Machine Learning Systems in Productionhttps://mlinproduction.com/5-challenges-to-ml-in-production-solve-them-with-aws-sagemaker/
Article: Enabling Machine-Learning-as-a-Service Through Privacy Preserving Machine Learninghttps://mlinproduction.com/enabling-machine-learning-as-service-through-privacy-preserving-ml/
Article: What Does it Mean to Deploy a Machine Learning Model? (Deployment Series: Guide 01)https://mlinproduction.com/what-does-it-mean-to-deploy-a-machine-learning-model-deployment-series-01/
Article: Software Interfaces for Machine Learning Deployment (Deployment Series: Guide 02)https://mlinproduction.com/software-interfaces-for-machine-learning-deployment-deployment-series-02/
Article: Batch Inference for Machine Learning Deployment (Deployment Series: Guide 03)https://mlinproduction.com/batch-inference-for-machine-learning-deployment-deployment-series-03/
Article: The Challenges of Online Inference (Deployment Series: Guide 04)https://mlinproduction.com/the-challenges-of-online-inference-deployment-series-04/
Article: Online Inference for ML Deployment (Deployment Series: Guide 05)https://mlinproduction.com/online-inference-for-ml-deployment-deployment-series-05/
Article: Model Registries for ML Deployment (Deployment Series: Guide 06)https://mlinproduction.com/model-registries-for-ml-deployment-deployment-series-06/
Article: Test-Driven Machine Learning Development (Deployment Series: Guide 07)https://mlinproduction.com/testing-machine-learning-models-deployment-series-07/
Article: A/B Testing Machine Learning Models (Deployment Series: Guide 08)https://mlinproduction.com/ab-test-ml-models-deployment-series-08/
Article: Lessons Learned from 15 Years of Monitoring Machine Learning in Productionhttps://mlinproduction.com/lessons-learned-from-15-years-of-monitoring-machine-learning-in-production/
Article: Why is it Important to Monitor Machine Learning Models?https://mlinproduction.com/why-is-it-important-to-monitor-machine-learning-models/
Article: Maximizing Business Impact with Machine Learninghttps://mlinproduction.com/maximizing-business-impact-with-machine-learning/
Codecademy: Deploy a Websitehttps://www.codecademy.com/learn/deploy-a-website
Datacamp: Parallel Computing with Daskhttps://www.datacamp.com/courses/parallel-computing-with-dask
Datacamp: Cloud Computing for Everyonehttps://www.datacamp.com/courses/cloud-computing-for-everyone
Django Best Practiceshttp://slides.com/sudipkafle/django-best-practices
Pluralsight: Docker and Containers: The Big Picturehttps://www.pluralsight.com/courses/docker-containers-big-picture
Pluralsight: Docker and Kubernetes: The Big Picturehttps://www.pluralsight.com/courses/docker-kubernetes-big-picture
Pluralsight: AWS Developer: The Big Picturehttps://www.pluralsight.com/courses/aws-developer-big-picture
Pluralsight: AWS Networking Deep Dive: Virtual Private Cloud (VPC)https://www.pluralsight.com/courses/aws-networking-deep-dive-vpc
Pluralsight: AWS VPC Operationshttps://www.pluralsight.com/courses/aws-vpc-operations
Pluralsight: Building Applications Using Elastic Beanstalkhttps://www.pluralsight.com/courses/elastic-beanstalk-building-applications
Servers for Hackers Serieshttps://serversforhackers.com/
The Hacker's Guide to Scaling Pythonhttps://scaling-python.com/
Udacity: HTTP & Web Servershttps://www.udacity.com/course/http-web-servers--ud303
Udacity: Intro to DevOpshttps://www.udacity.com/course/intro-to-devops--ud611
Udacity: Developing Scalable Apps in Pythonhttps://www.udacity.com/course/developing-scalable-apps-in-python--ud858
Udacity: Configuring Linux Web Servershttps://www.udacity.com/course/configuring-linux-web-servers--ud299
Udacity: Scalable Microservices with Kuberneteshttps://www.udacity.com/course/scalable-microservices-with-kubernetes--ud615
Udemy: AWS Conceptshttps://www.udemy.com/aws-concepts
Udemy: Serverless Conceptshttps://www.udemy.com/serverless-concepts/
Udemy: AWS Certified Developer - Associate 2018https://www.udemy.com/aws-certified-developer-associate/
Udacity: Authentication & Authorization: OAuthhttps://www.udacity.com/course/authentication-authorization-oauth--ud330
Udacity: Designing RESTful APIshttps://www.udacity.com/course/designing-restful-apis--ud388
Udacity: Client-Server Communicationhttps://www.udacity.com/course/client-server-communication--ud897
Youtube: PyConBY 2020: Sebastian Ramirez - Serve ML models easily with FastAPIhttps://www.youtube.com/watch?v=z9K5pwb0rt8
Youtube: FastAPI from the ground uphttps://www.youtube.com/watch?v=3DLwPcrE5mA
Whitepaper: Architecting for the Cloud AWS Best Practiceshttps://d1.awsstatic.com/whitepapers/AWS_Cloud_Best_Practices.pdf
Whitepaper: AWS Well-Architected Frameworkhttps://d1.awsstatic.com/whitepapers/architecture/AWS_Well-Architected_Framework.pdf
Whitepaper: AWS Security Best Practiceshttps://d1.awsstatic.com/whitepapers/Security/AWS_Security_Best_Practices.pdf
Whitepaper: Blue/Green Deployments on AWShttps://d1.awsstatic.com/whitepapers/AWS_Blue_Green_Deployments.pdf
Whitepaper: Microservices on AWShttps://docs.aws.amazon.com/aws-technical-content/latest/microservices-on-aws/microservices-on-aws.pdf
Whitepaper: Optimizing Enterprise Economics with Serverless Architectureshttps://d1.awsstatic.com/whitepapers/optimizing-enterprise-economics-serverless-architectures.pdf
Whitepaper: Practicing Continuous Integration and Continuous Delivery on AWShttps://d1.awsstatic.com/whitepapers/DevOps/practicing-continuous-integration-continuous-delivery-on-AWS.pdf
Whitepaper: Running Containerized Microservices on AWShttps://d1.awsstatic.com/whitepapers/DevOps/running-containerized-microservices-on-aws.pdf
Whitepaper: Serverless Architectures with AWS Lambdahttps://d1.awsstatic.com/whitepapers/serverless-architectures-with-aws-lambda.pdf
https://github.com/StudyWithJeffrey/learning#be-able-to-perform-ab-testing
Datacamp: Customer Analytics & A/B Testing in Pythonhttps://www.datacamp.com/courses/customer-analytics-ab-testing-in-python
Udacity: A/B Testinghttps://www.udacity.com/course/ab-testing--ud257
Udacity: A/B Testing for Business Analystshttps://www.udacity.com/course/ab-testing--ud979
Youtube: A/B Testing - Simply Explainedhttps://www.youtube.com/watch?v=pRTAiluUP-8
Youtube: Hypothesis testing with Applications in Data Sciencehttps://www.youtube.com/watch?v=kx-pcQAPvoc
https://github.com/StudyWithJeffrey/learning#be-able-to-write-unit-tests
Article: Effective testing for machine learning systemshttps://www.jeremyjordan.me/testing-ml
Datacamp: Unit Testing for Data Science in Pythonhttps://www.datacamp.com/courses/unit-testing-for-data-science-in-python
Pluralsight: Test-driven Development: The Big Picturehttps://www.pluralsight.com/courses/test-driven-development-big-picture
Test Driven Development with Pythonhttp://chimera.labs.oreilly.com/books/1234000000754/index.html
Thoughtbot: Fundamentals of TDDhttps://thoughtbot.com/upcase/fundamentals-of-tdd
Treehouse: Python Testinghttps://teamtreehouse.com/library/python-testing
Udacity: Software Analysis & Testinghttps://www.udacity.com/course/software-analysis-testing--ud333
Udacity: Software Testinghttps://www.udacity.com/course/software-testing--cs258
Udacity: Software Debugginghttps://www.udacity.com/course/software-debugging--cs259
https://github.com/StudyWithJeffrey/learning#be-proficient-in-python
Article: No Really, Python's Pathlib is Greathttps://rednafi.github.io/digressions/python/2020/04/13/python-pathlib.html
Book: A Byte of Pythonhttps://python.swaroopch.com
Book: Learn Python The Hard wayhttps://learnpythonthehardway.org
Book: Python 201https://leanpub.com/python201
Book: Python Anti-Patternshttps://docs.quantifiedcode.com/python-anti-patterns/index.html
Book: Real Pythonhttps://www.goodreads.com/book/show/20750754-real-python
Book: The Python 3 Standard Library By Examplehttps://doughellmann.com/blog/the-python-3-standard-library-by-example
Book: Writing Idiomatic Python 3https://www.amazon.com/Writing-Idiomatic-Python-Jeff-Knupp-ebook/dp/B00B5VXMRG
Codecademy: Learn Pythonhttps://www.codecademy.com/learn/learn-python
Cognitiveclass.ai: Python for Data Sciencehttps://cognitiveclass.ai/courses/python-for-data-science
Datacamp: Python for R Usershttps://www.datacamp.com/courses/python-for-r-users
Datacamp: Python for Spreadsheet Usershttps://www.datacamp.com/courses/python-for-spreadsheet-users
Datacamp: Python for MATLAB Usershttps://www.datacamp.com/courses/python-for-matlab-users
Datacamp: Importing Data in Python (Part 1)https://www.datacamp.com/courses/importing-data-in-python-part-1
Datacamp: Intermediate Python for Data Sciencehttps://www.datacamp.com/courses/intermediate-python-for-data-science
Datacamp: Python Data Science Toolbox (Part 1)https://www.datacamp.com/courses/python-data-science-toolbox-part-1
Datacamp: Python Data Science Toolbox (Part 2)https://www.datacamp.com/courses/python-data-science-toolbox-part-2
Datacamp: Intro to Python for Financehttps://www.datacamp.com/courses/intro-to-python-for-finance
Datacamp: Writing Efficient Python Codehttps://www.datacamp.com/courses/writing-efficient-python-code
Datacamp: Writing Functions in Pythonhttps://www.datacamp.com/courses/writing-functions-in-python
Datacamp: Working with Dates and Times in Pythonhttps://www.datacamp.com/courses/working-with-dates-and-times-in-python
Datacamp: Object-Oriented Programming in Pythonhttps://datacamp.com/courses/object-oriented-programming-in-python
edX: Introduction to Python for Data Sciencehttps://www.edx.org/course/introduction-python-data-science-microsoft-dat208x-7
edX: Programming with Python for Data Sciencehttps://www.edx.org/course/programming-python-data-science-microsoft-dat210x-5
Google's Python Classhttps://developers.google.com/edu/python/
Treehouse: Python Basicshttps://teamtreehouse.com/library/python-basics
Treehouse: Python collectionshttps://teamtreehouse.com/library/python-collections-2
Treehouse: Date and Timehttps://teamtreehouse.com/library/dates-and-times-in-python
Treehouse: CSV And JSONhttps://teamtreehouse.com/library/csv-and-json-in-python
Treehouse: Functional Programming with Pythonhttps://teamtreehouse.com/library/functional-python
Treehouse: Python Decoratorshttps://teamtreehouse.com/library/python-decorators
Treehouse: Write Better Pythonhttps://teamtreehouse.com/library/write-better-python
Thoughtbot: Regular Expressionshttps://thoughtbot.com/upcase/regular-expressions
TheNewBoston: Python Programming Tutorialshttps://www.youtube.com/watch?v=4Mf0h3HphEA&list=PLEA1FEF17E1E5C0DA
Udacity: Introduction to Python Programminghttps://www.udacity.com/course/introduction-to-python--ud1110
Udacity: Programming Foundations with Pythonhttps://www.udacity.com/course/programming-foundations-with-python--ud036
Udacity: What is Programming?https://www.udacity.com/course/what-is-programming--ud994
https://github.com/StudyWithJeffrey/learning#be-familiar-with-compiled-languages
Codecademy: Learn Javahttps://www.codecademy.com/learn/learn-java
Udacity: C++ For Programmershttps://www.udacity.com/course/c-for-programmers--ud210
Udacity: Java Programming Basicshttps://www.udacity.com/course/java-programming-basics--ud282
https://github.com/StudyWithJeffrey/learning#have-a-general-understanding-of-other-parts-of-the-stack
Book: Refactoring UIhttps://refactoringui.com/book/
Codecademy: Learn HTMLhttps://www.codecademy.com/learn/learn-html
Codecademy: Learn Color Designhttps://www.codecademy.com/learn/learn-color-design
Codecademy: Learn SASShttps://www.codecademy.com/learn/learn-sass
Codecademy: Make a websitehttps://www.codecademy.com/en/courses/make-a-website
Codecademy: Learn ReactJS: Part Ihttps://www.codecademy.com/learn/react-101
Codecademy: Learn ReactJS: Part IIhttps://www.codecademy.com/learn/react-102
Codecademy: Learn JavaScripthttps://www.codecademy.com/learn/learn-javascript
Codecademy: Jquery Trackhttps://www.codecademy.com/learn/learn-jquery
Codecademy: Learn Rubyhttps://www.codecademy.com/learn/learn-ruby
Code School: Fundamentals of Designhttps://www.pluralsight.com/courses/code-school-fundamentals-of-design
Code School: Blasting Off with Bootstraphttps://www.pluralsight.com/courses/code-school-blasting-off-with-bootstrap
(ES6) - Beau teaches JavaScripthttps://www.youtube.com/watch?v=1mgLWu69ijU&list=PLWKjhJtqVAbljtmmeS0c-CEl2LdE-eR_F
Pluralsight: UX Fundamentalshttps://www.pluralsight.com/courses/ux-fundamentals-2426
Pluralsight: HTML, CSS, and JavaScript: The Big Picturehttps://app.pluralsight.com/library/courses/html-css-javascript-big-picture
Pluralsight: CSS Positioninghttps://www.pluralsight.com/courses/css-positioning-1834
Pluralsight: Introduction to CSShttps://www.pluralsight.com/courses/css-intro
Pluralsight: CSS: Specificity, the Box Model, and Best Practiceshttps://app.pluralsight.com/interactive-courses/detail/c580b092-d94a-4ed8-8d2a-2f4d0b76f99f
Pluralsight: CSS: Using Flexbox for Layouthttps://app.pluralsight.com/interactive-courses/detail/a089d0a5-4a4c-4c4e-b883-c1bc64009619
Pluralsight: Using The Chrome Developer Toolshttps://www.pluralsight.com/courses/chrome-developer-tools
Thoughtbot: Design for Developershttps://thoughtbot.com/upcase/design-for-developers
Treehouse: HTMLhttps://teamtreehouse.com/library/html
Treehouse: Javascript Booleanshttps://teamtreehouse.com/library/javascript-booleans
Udacity: ES6 - JavaScript Improvedhttps://www.udacity.com/course/es6-javascript-improved--ud356
Udacity: Intro to Javascripthttps://www.udacity.com/course/intro-to-javascript--ud803
Udacity: Object Oriented JS 1https://www.udacity.com/course/object-oriented-javascript--ud015
Udacity: Object Oriented JS 2https://www.udacity.com/course/object-oriented-javascript--ud711
Udemy: Understanding Typescripthttps://www.udemy.com/understanding-typescript/
https://github.com/StudyWithJeffrey/learning#be-familiar-with-fundamental-computer-science-concepts
Codecademy: Big Ohttps://www.codecademy.com/courses/big-o/0/1
Crashcourse: Computer Sciencehttps://www.youtube.com/playlist?list=PL8dPuuaLjXtNlUrzyH5r6jN9ulIgZBpdo
Grokking Algorithmshttps://www.manning.com/books/grokking-algorithms
Khan Academy: Data Structureshttps://www.khanacademy.org/computing/computer-science/algorithms
Udacity: Intro to Algorithmshttps://www.udacity.com/course/intro-to-algorithms--cs215
Udacity: Intro to Computer Sciencehttps://www.udacity.com/course/intro-to-computer-science--cs101
Udacity: Intro to Theoretical Computer Sciencehttps://www.udacity.com/courses/cs313
Udacity: Programming Languageshttps://www.udacity.com/course/programming-languages--cs262
Udacity: Networking for Web Developershttps://www.udacity.com/course/networking-for-web-developers--ud256
https://github.com/StudyWithJeffrey/learning#be-able-to-apply-proper-software-engineering-process
Launch School: Agile Planninghttps://launchschool.com/books/agile_planning
Pluralsight: Product Owner Fundamentalshttps://www.pluralsight.com/courses/product-owner-fundamentals-foundations
Pluralsight: Scrum Master Fundamentals - Foundationshttps://www.pluralsight.com/courses/scrum-master-fundamentals-foundations
Pluralsight: Security Awareness: Basic Concepts and Terminologyhttps://app.pluralsight.com/library/courses/security-awareness-basic-concepts-terminology
Pluralsight: Secure Software Developmenthttps://www.pluralsight.com/courses/software-development-secure
Pluralsight: Clean Architecture: Patterns, Practices, and Principleshttps://www.pluralsight.com/courses/clean-architecture-patterns-practices-principles
Thoughtbot: Software Development Processhttps://thoughtbot.com/upcase/the-playbook-video-edition
Thoughtbot: Refactoringhttps://thoughtbot.com/upcase/refactoring
Udacity: Design of Computer Programshttps://www.udacity.com/course/design-of-computer-programs--cs212
Udacity: Product Designhttps://www.udacity.com/course/product-design--ud509
Udacity: Rapid Prototypinghttps://www.udacity.com/course/rapid-prototyping--ud723
Udacity: Software Architecture and Designhttps://www.udacity.com/course/software-architecture-design--ud821
Udacity: Software Development Processhttps://www.udacity.com/course/software-development-process--ud805
Udacity: Full Stack Foundationshttps://www.udacity.com/course/full-stack-foundations--ud088
https://github.com/StudyWithJeffrey/learning#be-able-to-efficiently-use-a-text-editor
Learn Enough Text Editor to Be Dangeroushttps://www.learnenough.com/text-editor-tutorial
Mastering Pycharmhttps://www.amazon.com/Mastering-PyCharm-Quazi-Nafiul-Islam-ebook/dp/B00YSIKR3A
https://github.com/StudyWithJeffrey/learning#be-able-to-communicate-and-collaborate-well
Google: Technical Writinghttps://developers.google.com/tech-writing
Book: Emotional Intelligencehttps://www.amazon.com/Emotional-Intelligence-Matter-More-Than/dp/055338371X
Book: How to Win Friends & Influence Peoplehttps://www.amazon.com/How-Win-Friends-Influence-People/dp/0671027034
Book: Influence: The Psychology of Persuasionhttps://www.goodreads.com/book/show/28815.Influence
Book: Leaders Eat Last: Why Some Teams Pull Together and Others Don'thttps://www.amazon.com/Leaders-Eat-Last-Together-Others/dp/1591848016
Book: Multipliers: How the Best Leaders Make Everyone Smarterhttps://www.amazon.com/Multipliers-Best-Leaders-Everyone-Smarter/dp/0061964395
Book: Soft Skills: The software developer's life manualhttps://www.amazon.com/Soft-Skills-software-developers-manual/dp/1617292397
Book: The New One Minute Managerhttps://www.amazon.com/New-One-Minute-Manager-ebook/dp/B00MMG19OG
Youtube: Building a psychologically safe workplace | Amy Edmondson | TEDxHGSEhttps://youtu.be/LhoLuui9gX8
https://github.com/StudyWithJeffrey/learning#be-familiar-with-the-hiring-pipeline
Datacamp: Preparing for Statistics Interview Questions in Pythonhttps://www.datacamp.com/courses/preparing-for-statistics-interview-questions-in-python
Datacamp: Preparing for Coding Interview Questions in Pythonhttps://www.datacamp.com/courses/preparing-for-coding-interview-questions-in-python
Udacity: Optimize your GitHubhttps://www.udacity.com/course/optimize-your-github--ud247
Udacity: Strengthen Your LinkedIn Network & Brandhttps://eu.udacity.com/course/strengthen-your-linkedin-network-and-brand--ud242
Udacity: Data Science Interview Prephttps://www.udacity.com/course/data-science-interview-prep--ud944
Udacity: Full-Stack Interview Prephttps://www.udacity.com/course/full-stack-interview-prep--ud252
Udacity: Refresh Your Resumehttps://www.udacity.com/course/refresh-your-resume--ud243
Udacity: Craft Your Cover Letterhttps://www.udacity.com/course/craft-your-cover-letter--ud244
Udacity: Technical Interviewhttps://www.udacity.com/course/technical-interview--ud513
Youtube: Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papershttps://www.youtube.com/watch?v=733m6qBH-jI
https://github.com/StudyWithJeffrey/learning#broaden-perspective
Book: Atomic Habitshttps://www.amazon.com/Atomic-Habits-Proven-Build-Break/dp/0735211299
Book: Deep Workhttps://www.amazon.com/Deep-Work-Focused-Success-Distracted/dp/1455586692
Book: Outliers: The Story of Successhttps://www.amazon.com/Outliers-Story-Success-Malcolm-Gladwell/dp/0316017930
Book: Rich Dad Poor Dadhttps://www.amazon.com/Rich-Dad-Poor-Teach-Middle/dp/1543626610
Book: The Power of Brokehttps://www.goodreads.com/book/show/25430691-the-power-of-broke
Book: The 10X Rulehttps://www.amazon.com/10X-Rule-Difference-Between-Success/dp/0470627603
Book: The Millionaire Fastlanehttps://www.amazon.com/Millionaire-Fastlane-Crack-Wealth-Lifetime/dp/0984358102
Book: The Subtle Art of Not Giving a F**khttps://www.amazon.com/Subtle-Art-Not-Giving-Counterintuitive/dp/0062457713
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