深度学习教程 TensorFlow and Deep Learning Tutorials

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 [门函数]

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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