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Title: GitHub - castellwhite/AppliedDeepLearningClass: Class Applied Deep Learning - Summer 2018

Open Graph Title: GitHub - castellwhite/AppliedDeepLearningClass: Class Applied Deep Learning - Summer 2018

X Title: GitHub - castellwhite/AppliedDeepLearningClass: Class Applied Deep Learning - Summer 2018

Description: Class Applied Deep Learning - Summer 2018. Contribute to castellwhite/AppliedDeepLearningClass development by creating an account on GitHub.

Open Graph Description: Class Applied Deep Learning - Summer 2018. Contribute to castellwhite/AppliedDeepLearningClass development by creating an account on GitHub.

X Description: Class Applied Deep Learning - Summer 2018. Contribute to castellwhite/AppliedDeepLearningClass development by creating an account on GitHub.

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sa.angulo@uniandes.edu.co

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https://patch-diff.githubusercontent.com/castellwhite/AppliedDeepLearningClass#resources
Deep learninghttp://www.deeplearningbook.org/
Machine Learninghttps://fagonzalezo.github.io/ml-2018-1/
Deep Learning for Text Analysis and Understandinghttps://fagonzalezo.github.io/dl-tau-2017-2/
Representation Learning and Deep Learning Tutorialhttps://fagonzalezo.github.io/dl_tutorial_upv/
https://patch-diff.githubusercontent.com/castellwhite/AppliedDeepLearningClass#schedule
https://patch-diff.githubusercontent.com/castellwhite/AppliedDeepLearningClass#introduction-to-machine-learning-and-neural-networks
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2 - Intro to Python for data analysishttp://nbviewer.jupyter.org/github/albahnsen/AppliedDeepLearningClass/blob/master/notebooks/02-IntroPython_Numpy_Scypy_Pandas.ipynb
3 - Linear Regressionhttp://nbviewer.jupyter.org/github/albahnsen/AppliedDeepLearningClass/blob/master/notebooks/03-linear_regression.ipynb
4 - Logistic Regressionhttp://nbviewer.jupyter.org/github/albahnsen/AppliedDeepLearningClass/blob/master/notebooks/04-logistic_regression.ipynb
E1 - Python&Pandashttp://nbviewer.jupyter.org/github/albahnsen/AppliedDeepLearningClass/blob/master/exercises/E01-Python%26Numpy%26Pandas.ipynb
E2 - Regressionhttp://nbviewer.jupyter.org/github/albahnsen/AppliedDeepLearningClass/blob/master/exercises/E02-Regression-IncomePrediction.ipynb
5 - Data preparation and model evaluationhttp://nbviewer.jupyter.org/github/albahnsen/AppliedDeepLearningClass/blob/master/notebooks/05-data_preparation_evaluation.ipynb
6 - Samplinghttp://nbviewer.jupyter.org/github/albahnsen/AppliedDeepLearningClass/blob/master/notebooks/06_Unbalanced_Datasets.ipynb
7 - Ensemble methodshttp://nbviewer.jupyter.org/github/albahnsen/AppliedDeepLearningClass/blob/master/notebooks/07_EnsembleMethods.ipynb
8 - Model deploymenthttp://nbviewer.jupyter.org/github/albahnsen/AppliedDeepLearningClass/blob/master/notebooks/08_Model_Deployment.ipynb
E3 - Cross Validationhttp://nbviewer.jupyter.org/github/albahnsen/AppliedDeepLearningClass/blob/master/exercises/E03-CrossVal-CreditScoring.ipynb
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https://patch-diff.githubusercontent.com/castellwhite/AppliedDeepLearningClass#introduction-to-deep-learning
12-Intro to Deep Learninghttps://github.com/albahnsen/AppliedDeepLearningClass/blob/master/presentations/DL-introduction.pdf
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