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Title: GitHub - chen115y/MLOpsLearning: This repository provides some basic training materials for data scientists

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GitHubhttps://github.com/chen115y/DataScience_Env_Setup
Google Colaboratory (colab)https://colab.research.google.com/notebooks/intro.ipynb
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Visual Studio Codehttps://code.visualstudio.com/docs/python/jupyter-support
Anacondahttps://www.anaconda.com/distribution/
https://patch-diff.githubusercontent.com/chen115y/MLOpsLearning#basic-knowledge
https://patch-diff.githubusercontent.com/chen115y/MLOpsLearning#beginners-resource
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Microsoft ML for Beginnershttps://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst
Microsoft Data Science for Beginnershttps://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst
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Khan Academy - Mathhttps://www.khanacademy.org/math
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https://patch-diff.githubusercontent.com/chen115y/MLOpsLearning#data-science-basics
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https://patch-diff.githubusercontent.com/chen115y/MLOpsLearning#deep-learning-basics
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Activation Functionshttps://www.analyticsvidhya.com/blog/2020/01/fundamentals-deep-learning-activation-functions-when-to-use-them/
Traning Deep Neural Networkhttps://github.com/ageron/handson-ml2/blob/master/11_training_deep_neural_networks.ipynb
Convolutional Neural Network (CNN)https://github.com/ageron/handson-ml2/blob/master/14_deep_computer_vision_with_cnns.ipynb
Recurrent Neural Network (RNN)https://github.com/ageron/handson-ml2/blob/master/15_processing_sequences_using_rnns_and_cnns.ipynb
Autoencoder and Generative Adversarial Network (GAN)https://github.com/ageron/handson-ml2/blob/master/17_autoencoders_and_gans.ipynb
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https://patch-diff.githubusercontent.com/chen115y/MLOpsLearning#natural-language-processing-nlp
Word Embedding - Word2Vechttps://gist.github.com/aparrish/2f562e3737544cf29aaf1af30362f469
Advanced NLP with SpaCyhttps://course.spacy.io/en/
Natural Language Processing with RNNs and Attentionhttps://github.com/ageron/handson-ml2/blob/master/16_nlp_with_rnns_and_attention.ipynb
Transformer Introductionhttps://patch-diff.githubusercontent.com/chen115y/MLOpsLearning/blob/master/NLP/transformers.ipynb
Transformer model for language understandinghttps://patch-diff.githubusercontent.com/chen115y/MLOpsLearning/blob/master/NLP/transformer.ipynb
How do Transformers Work in NLP? A Guide to the Latest State-of-the-Art Modelshttps://www.analyticsvidhya.com/blog/2019/06/understanding-transformers-nlp-state-of-the-art-models/
The Illustrated Transformerhttp://jalammar.github.io/illustrated-transformer/
A Visual Guide to Using BERT for the First Timehttp://jalammar.github.io/a-visual-guide-to-using-bert-for-the-first-time/
Applying massive language models in the real world with Coherehttps://jalammar.github.io/applying-large-language-models-cohere/
Intuition Builder: How to Wrap Your Mind Around Transformer’s Attention Mechanismhttps://towardsdatascience.com/how-to-explain-transformer-self-attention-like-i-am-five-9020bf50b764
https://patch-diff.githubusercontent.com/chen115y/MLOpsLearning#generative-ai
How to Build OpenAI's GPT-2: "The AI That Was Too Dangerous to Release"https://blog.floydhub.com/gpt2/
The Illustrated GPT-2 (Visualizing Transformer Language Models)http://jalammar.github.io/illustrated-gpt2/
How GPT3 Works - Visualizations and Animationshttps://jalammar.github.io/how-gpt3-works-visualizations-animations/
The Illustrated Stable Diffusionhttps://jalammar.github.io/illustrated-stable-diffusion/
LMFlow - An extensible, convenient, and efficient toolbox for finetuning large machine learning modelshttps://github.com/OptimalScale/LMFlow
LLaMA-Adapter: Efficient Fine-tuning of LLaMAhttps://github.com/zrrskywalker/llama-adapter
List of Open Sourced Fine-Tuned Large Language Models (LLM)https://medium.com/geekculture/list-of-open-sourced-fine-tuned-large-language-models-llm-8d95a2e0dc76
Google Cloud - Generative AI learning pathhttps://www.cloudskillsboost.google/paths/118
Lil’Log - Lilian is leading a team on AI Safety at OpenAI.https://lilianweng.github.io/
OpenAI Cookbookhttps://github.com/openai/openai-cookbook
Prompt Engineering Guidehttps://github.com/dair-ai/Prompt-Engineering-Guide
Awesome Prompt Engineeringhttps://github.com/promptslab/Awesome-Prompt-Engineering
(Almost) Everything I know about LLMshttps://barryz-architecture-of-agentic-llm.notion.site/Almost-Everything-I-know-about-LLMs-d117ca25d4624199be07e9b0ab356a77
I-JEPA: The first AI model based on Yann LeCun’s vision for more human-like AIhttps://ai.facebook.com/blog/yann-lecun-ai-model-i-jepa/?utm_source=linkedin&utm_medium=organic_social&utm_campaign=blog&utm_content=link
https://patch-diff.githubusercontent.com/chen115y/MLOpsLearning#retrieval-augmented-generation-rag
RAG: Retrieval-Augmented Generation - OpenAIhttps://openai.com/blog/retrieval-augmented-generation/
GitHub RAG Techniqueshttps://github.com/NirDiamant/RAG_Techniques
https://patch-diff.githubusercontent.com/chen115y/MLOpsLearning#data-centric-ai
Data-centric AI - MIThttps://www.youtube.com/playlist?app=desktop&list=PLnSYPjg2dHQKdig0vVbN-ZnEU0yNJ1mo5
Interfaces for Explaining Transformer Language Modelshttps://jalammar.github.io/explaining-transformers/
Explainable AI Cheat Sheethttps://jalammar.github.io/explainable-ai/
Unveiling the Black Box model using Explainable AI(Lime, Shap) Industry use casehttps://www.analyticsvidhya.com/blog/2020/10/unveiling-the-black-box-model-using-explainable-ai-lime-shap-industry-use-case/
What Are the Data-Centric AI Concepts behind GPT Models?https://towardsdatascience.com/what-are-the-data-centric-ai-concepts-behind-gpt-models-a590071bb727
https://patch-diff.githubusercontent.com/chen115y/MLOpsLearning#reinforcement-learning-with-human-feedbacks
Reinforcement Learning - An Introductionhttps://github.com/ageron/handson-ml2/blob/master/18_reinforcement_learning.ipynb
llustrating Reinforcement Learning from Human Feedback (RLHF)https://huggingface.co/blog/rlhf
StackLLaMA: A hands-on guide to train LLaMA with RLHFhttps://huggingface.co/blog/stackllama
ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipelinehttps://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b
Mastering RLHF with AWS: A Hands-on Workshop on Reinforcement Learning from Human Feedbackhttps://www.youtube.com/watch?v=-0pvrCLd2Ak
https://patch-diff.githubusercontent.com/chen115y/MLOpsLearning#data-feature-engineering-tools
Numpyhttps://patch-diff.githubusercontent.com/chen115y/MLOpsLearning/blob/master/DataWrangling/Numpy.ipynb
Pandashttps://patch-diff.githubusercontent.com/chen115y/MLOpsLearning/blob/master/DataWrangling/Pandas.ipynb
Polarshttps://github.com/pola-rs/polars
PySparkhttps://patch-diff.githubusercontent.com/chen115y/MLOpsLearning/blob/master/DataWrangling/PySpark.ipynb
Data Profiling Toolshttps://towardsdatascience.com/awesome-data-science-tools-to-master-in-2023-data-profiling-edition-29d29310f779
Cheat Sheetshttps://github.com/chen115y/DESAL/tree/master/CheatSheets/Python
https://patch-diff.githubusercontent.com/chen115y/MLOpsLearning#data-visualization-tools
Matplotlibhttps://patch-diff.githubusercontent.com/chen115y/MLOpsLearning/blob/master/Visualization/Matplotlib.ipynb
Seabornhttps://patch-diff.githubusercontent.com/chen115y/MLOpsLearning/blob/master/Visualization/Seaborn.ipynb
Plotlyhttps://github.com/plotly/plotly.py
Cheat Sheetshttps://github.com/chen115y/DESAL/tree/master/CheatSheets/Visualization
https://patch-diff.githubusercontent.com/chen115y/MLOpsLearning#mlops-platforms---open-source
condahttps://github.com/conda/conda
https://camo.githubusercontent.com/267c3d13ea1876a4ad40c7b3b0a020fd7a778e84c9d7b85c69f72e8b7f39edc8/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f636f6e64612f636f6e64612e7376673f7374796c653d736f6369616c
kuberneteshttps://kubernetes.io/
kubeflowhttps://www.kubeflow.org/
MLFlowhttps://mlflow.org/
https://patch-diff.githubusercontent.com/chen115y/MLOpsLearning#mlops-platforms---cloud-vendors
Databricks - MLOpshttps://patch-diff.githubusercontent.com/chen115y/MLOpsLearning/blob/master/The-Big-Book-of-MLOps-v6.pdf
AWS SageMakerhttps://aws.amazon.com/sagemaker/
GCP Vertex AIhttps://cloud.google.com/vertex-ai
Azure Machine Learninghttps://azure.microsoft.com/en-us/products/machine-learning/
https://patch-diff.githubusercontent.com/chen115y/MLOpsLearning#recommended-youtube-videos
Illustrated Guide to Transformers Neural Network: A step by step explanationhttps://www.youtube.com/watch?v=4Bdc55j80l8
Amazon Machine Learning Universityhttps://www.youtube.com/channel/UC12LqyqTQYbXatYS9AA7Nuw
Tensorflow - ML Zero to Herohttps://www.youtube.com/watch?v=KNAWp2S3w94&list=RDCMUC0rqucBdTuFTjJiefW5t-IQ&start_radio=1&t=5
Deep Learning with PyTorch - Full Coursehttps://www.youtube.com/watch?v=GIsg-ZUy0MY
Gradient Descent - Youtubehttps://www.youtube.com/watch?v=IHZwWFHWa-w
Backpropagation Calculus - Youtubehttps://www.youtube.com/watch?v=tIeHLnjs5U8
Illustrated Guide to Recurrent Neural Networks: Understanding the Intuitionhttps://www.youtube.com/watch?v=LHXXI4-IEns
Illustrated Guide to LSTM's and GRU's: A step by step explanationhttps://www.youtube.com/watch?v=8HyCNIVRbSU
Transformer Neural Networks - EXPLAINED! (Attention is all you need)https://www.youtube.com/watch?v=TQQlZhbC5ps
Illustrated Guide to Transformers Neural Network: A step by step explanationhttps://www.youtube.com/watch?v=4Bdc55j80l8
LSTM is dead. Long Live Transformers!https://www.youtube.com/watch?v=S27pHKBEp30
BERT Neural Network - EXPLAINED!https://www.youtube.com/watch?v=xI0HHN5XKDo
GPT-3: Language Models are Few-Shot Learners (Paper Explained)https://www.youtube.com/watch?v=SY5PvZrJhLE
https://patch-diff.githubusercontent.com/chen115y/MLOpsLearning#related-free-on-line-resources
OpenAI Gymhttps://github.com/dennybritz/reinforcement-learning
AWS SageMaker Exampleshttps://github.com/aws/amazon-sagemaker-examples/tree/main/reinforcement_learning
Scaling Laws for Neural Language Modelshttps://arxiv.org/pdf/2001.08361
Google Python Classhttps://developers.google.com/edu/python
Online Python Tutorialshttps://pythonspot.com/
Interactive Python Classhttps://cscircles.cemc.uwaterloo.ca/
Algorithms and Data Structures using Pythonhttps://runestone.academy/runestone/books/published/pythonds/index.html
websitehttps://jakevdp.github.io/PythonDataScienceHandbook/
Githubhttps://github.com/jakevdp/PythonDataScienceHandbook
1st Editionhttp://index-of.es/Varios-2/Hands%20on%20Machine%20Learning%20with%20Scikit%20Learn%20and%20Tensorflow.pdf
Githubhttps://github.com/ageron/handson-ml2
Githubhttps://github.com/aws/amazon-sagemaker-examples
Tensorflowhttps://www.tensorflow.org/
PyTorchhttps://pytorch.org/
MXNethttps://mxnet.apache.org/
An Introduction to Math Behind Neural Networkhttps://hackernoon.com/a-6ur13zzx
Machine Learning & Deep Learning Fundamentals - Youtubehttps://www.youtube.com/playlist?list=PLZbbT5o_s2xq7LwI2y8_QtvuXZedL6tQU
Spark Data Processing Coursehttps://github.com/luisbelloch/data_processing_course
Cornell University CS4780/CS5780 Course: Machine Learning for Intelligent Systemshttp://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/index.html
"Fake" Data Scientistshttps://towardsdatascience.com/from-sklearn-import-478c711dafa1
You can master Computer Vision, Deep Learning, and OpenCV - Adrian Rosebrock, PhDhttps://www.pyimagesearch.com/
Data Scientist Interview Questionshttps://patch-diff.githubusercontent.com/chen115y/MLOpsLearning/blob/master/DataScienceInterviewQuestions.pdf
Stanford CS324 - understanding and developing large language modelshttps://stanford-cs324.github.io/winter2022/lectures/introduction/
Computer Science Courses with Video Lectureshttps://github.com/Developer-Y/cs-video-courses
Readme https://patch-diff.githubusercontent.com/chen115y/MLOpsLearning#readme-ov-file
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