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Some Tensorflow tutorial materials

[https://devhub.io/repos/wagamamaz-tensorflow-tutorial]

TensorFlow and Deep Learning Tutorials



Google's Deep Learning Tutorials

Deep Learning Reading List

Tutorial index

0 - Prerequisite

  • Introduction to Machine Learning (notebook)
  • Introduction to MNIST Dataset (notebook)

1 - Introduction

2 - Basic Models

3 - Neural Networks

4 - Utilities

  • Save and Restore a model (notebook) (code)
  • Tensorboard - Graph and loss visualization (notebook) (code)
  • Tensorboard - Advanced visualization (code)

5 - Multi GPU

Dataset

Some examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py). MNIST is a database of handwritten digits, for a quick description of that dataset, you can check this notebook.

Official Website: http://yann.lecun.com/exdb/mnist/

Selected Repositories

Examples

Basics

  • Multi-layer perceptron (MNIST). A multi-layer perceptron implementation for MNIST classification task, see tutorial_mnist_simple.py here.

Computer Vision

  • Denoising Autoencoder (MNIST). A multi-layer perceptron implementation for MNIST classification task, see tutorial_mnist.py here.
  • Stacked Denoising Autoencoder and Fine-Tuning (MNIST). A multi-layer perceptron implementation for MNIST classification task, see tutorial_mnist.py here.
  • Convolutional Network (MNIST). A Convolutional neural network implementation for classifying MNIST dataset, see tutorial_mnist.pyhere.
  • Convolutional Network (CIFAR-10). A Convolutional neural network implementation for classifying CIFAR-10 dataset, see tutorial_cifar10.py here.
  • VGG 16 (ImageNet). A Convolutional neural network implementation for classifying ImageNet dataset, see tutorial_vgg16.py here.
  • VGG 19 (ImageNet). A Convolutional neural network implementation for classifying ImageNet dataset, see tutorial_vgg19.py here.

Natural Language Processing

  • Recurrent Neural Network (LSTM). Apply multiple LSTM to PTB dataset for language modeling, see tutorial_ptb_lstm.py here.
  • Word Embedding - Word2vec. Train a word embedding matrix, see tutorial_word2vec_basic.py here.
  • Restore Embedding matrix. Restore a pre-train embedding matrix, see tutorial_generate_text.py here.
  • Text Generation. Generates new text scripts, using LSTM network, see tutorial_generate_text.py here.
  • Machine Translation (WMT). Translate English to French. Apply Attention mechanism and Seq2seq to WMT English-to-French translation data, see tutorial_translate.py here.

Reinforcement Learning

  • Deep Reinforcement Learning - Pong Game. Teach a machine to play Pong games, see tutorial_atari_pong.py here.
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