Deep Learning With PyTorch and TensorFlow

Basics

• Deep learning core concepts.
• Deep learning training.
• Understanding LSTM networks
• The Unreasonable Effectiveness of Recurrent Neural Networks shows a bunch of real life examples
• Deep Learning, NLP, and Representations for an overview on word embeddings and RNNs for NLP
• Understanding LSTM Networks is about LSTMs work specifically, but also informative about RNNs in general
• Calculus on Computational Graphs

Deep Learning with PyTorch

PyTorch 0.3 is recommended.

1 - PyTorch basics

• Offical PyTorch tutorials for more tutorials (some of these tutorials are included there)
• Deep Learning with PyTorch: A 60-minute Blitz to get started with PyTorch in general
• PyTorch Basics
• Linear Regression
• Logistic Regression
• Feedforward Neural Network
• Introduction to PyTorch for former Torchies if you are a former Lua Torch user

2 - Intermediate

• Convolutional Neural Network
• Deep Residual Network
• Recurrent Neural Network
• Bidirectional Recurrent Neural Network
• Language Model (RNN-LM)
• Generative Adversarial Network

3 - Advanced

• Image Captioning (CNN-RNN)
• Deep Convolutional GAN (DCGAN)
• Variational Auto-Encoder
• Neural Style Transfer

4 - Utilities

• TensorBoard in PyTorch

More Examples

• spro/practical-pytorch
• jcjohnson's PyTorch examples for a more in depth overview (including custom modules and autograd functions)
• chenyuntc/pytorch-book

Deep Learning with TensorFlow

TensorFlow v1.4 is recommended. Added many new examples (kmeans, random forest, multi-gpu training, layers api, estimator api, dataset api ...).

1 - Introduction

• Hello World (notebook) (code). Very simple example to learn how to print "hello world" using TensorFlow.
• Basic Operations (notebook) (code). A simple example that cover TensorFlow basic operations.

2 - Basic Models

• Linear Regression (notebook) (code). Implement a Linear Regression with TensorFlow.
• Logistic Regression (notebook) (code). Implement a Logistic Regression with TensorFlow.
• Nearest Neighbor (notebook) (code). Implement Nearest Neighbor algorithm with TensorFlow.
• K-Means (notebook) (code). Build a K-Means classifier with TensorFlow.
• Random Forest (notebook) (code). Build a Random Forest classifier with TensorFlow.

3 - Neural Networks

Supervised

• Simple Neural Network (notebook) (code). Build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset. Raw TensorFlow implementation.
• Simple Neural Network (tf.layers/estimator api) (notebook) (code). Use TensorFlow 'layers' and 'estimator' API to build a simple neural network (a.k.a Multi-layer Perceptron) to classify MNIST digits dataset.
• Convolutional Neural Network (notebook) (code). Build a convolutional neural network to classify MNIST digits dataset. Raw TensorFlow implementation.
• Convolutional Neural Network (tf.layers/estimator api) (notebook) (code). Use TensorFlow 'layers' and 'estimator' API to build a convolutional neural network to classify MNIST digits dataset.
• Recurrent Neural Network (LSTM) (notebook) (code). Build a recurrent neural network (LSTM) to classify MNIST digits dataset.
• Bi-directional Recurrent Neural Network (LSTM) (notebook) (code). Build a bi-directional recurrent neural network (LSTM) to classify MNIST digits dataset.
• Dynamic Recurrent Neural Network (LSTM) (notebook) (code). Build a recurrent neural network (LSTM) that performs dynamic calculation to classify sequences of different length.

Unsupervised

• Auto-Encoder (notebook) (code). Build an auto-encoder to encode an image to a lower dimension and re-construct it.
• Variational Auto-Encoder (notebook) (code). Build a variational auto-encoder (VAE), to encode and generate images from noise.
• GAN (Generative Adversarial Networks) (notebook) (code). Build a Generative Adversarial Network (GAN) to generate images from noise.
• DCGAN (Deep Convolutional Generative Adversarial Networks) (notebook) (code). Build a Deep Convolutional Generative Adversarial Network (DCGAN) to generate images from noise.

4 - Utilities

• Save and Restore a model (notebook) (code). Save and Restore a model with TensorFlow.
• Tensorboard - Graph and loss visualization (notebook) (code). Use Tensorboard to visualize the computation Graph and plot the loss.
• Tensorboard - Advanced visualization (notebook) (code). Going deeper into Tensorboard; visualize the variables, gradients, and more...

5 - Data Management

• Build an image dataset (notebook) (code). Build your own images dataset with TensorFlow data queues, from image folders or a dataset file.
• TensorFlow Dataset API (notebook) (code). Introducing TensorFlow Dataset API for optimizing the input data pipeline.

6 - Multi GPU

• Basic Operations on multi-GPU (notebook) (code). A simple example to introduce multi-GPU in TensorFlow.
• Train a Neural Network on multi-GPU (notebook) (code). A clear and simple TensorFlow implementation to train a convolutional neural network on multiple GPUs.

More Examples

• aymericdamien/TensorFlow-Examples
• buomsoo-kim/Easy-deep-learning-with-Keras
• lawlite19/MachineLearning_Python
• apachecn/MachineLearning
• Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow
• lawlite19/DeepLearning_Python
• A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks
• Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech
• Content for Udacity's Machine Learning curriculum
• This is the lab repository of my honours degree project on machine learning
• A curated list of awesome Machine Learning frameworks, libraries and software
• Bare bones Python implementations of some of the fundamental Machine Learning models and algorithms
• The "Python Machine Learning" book code repository and info resource