Google's Deep Learning Tutorials

• TensorFlow Official Deep Learning Tutorial?[中文].
• MLP with Dropout?TensorFlow?[中文]?TensorLayer?[中文]
• Autoencoder?TensorLayer?[中文]
• Convolutional Neural Network?TensorFlow?[中文]?TensorLayer?[中文]
• Recurrent Neural Network?TensorFlow?[中文]?TensorLayer?[中文]
• Deep Reinforcement Learning?TensorLayer?[中文]
• Sequence to Sequence?TensorFlow?TensorLayer[中文]
• Word Embedding?TensorFlow?[中文]?TensorLayer?[中文]

Deep Learning Reading List

• MIT Deep Learning Book
• Karpathy Blog
• Stanford UFLDL Tutorials
• Colah's Blog - Word Embedding?[中文]
• Colah's Blog - Understand LSTN?[門函數]

Tutorial index

0 - Prerequisite

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

1 - Introduction

• Hello World (notebook) (code)
• Basic Operations (notebook) (code)

2 - Basic Models

• Nearest Neighbor (notebook) (code)
• Linear Regression (notebook) (code)
• Logistic Regression (notebook) (code)

3 - Neural Networks

• Multilayer Perceptron (notebook) (code)
• Convolutional Neural Network (notebook) (code)
• Recurrent Neural Network (LSTM) (notebook) (code)
• Bidirectional Recurrent Neural Network (LSTM) (notebook) (code)
• Dynamic Recurrent Neural Network (LSTM) (code)
• AutoEncoder (notebook) (code)

4 - Utilities

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

5 - Multi GPU

• Basic Operations on multi-GPU (notebook) (code)

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

• jtoy/awesome-tensorflow
• nlintz/TensorFlow-Tutoirals
• adatao/tensorspark
• ry/tensorflow-resnet

Tricks

• Tricks to use TensorLayer

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.py?here.
• 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.

Useful Links

• Tricks to use TensorLayer

from:?https://github.com/wagamamaz/tensorflow-tutorial/blob/master/README.md?hmsr=toutiao.io&utm_medium=toutiao.io&utm_source=toutiao.io

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