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
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