# tensorflow-tutorial **Repository Path**: ifquant/tensorflow-tutorial ## Basic Information - **Project Name**: tensorflow-tutorial - **Description**: TensorFlow and Deep Learning Tutorials - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-07-31 - **Last Updated**: 2021-07-31 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # TensorFlow and Deep Learning Tutorials


## Google's Deep Learning Tutorials - [TensorFlow Official Deep Learning Tutorial](https://www.tensorflow.org/versions/master/tutorials/index.html) [[中文]](http://wiki.jikexueyuan.com/project/tensorflow-zh/). - MLP with Dropout [TensorFlow](https://www.tensorflow.org/versions/master/tutorials/mnist/beginners/index.html) [[中文]](http://wiki.jikexueyuan.com/project/tensorflow-zh/tutorials/mnist_beginners.html) [TensorLayer](http://tensorlayer.readthedocs.io/en/latest/user/tutorial.html#tensorlayer-is-simple) [[中文]](http://tensorlayercn.readthedocs.io/zh/latest/user/tutorial.html#tensorlayer) - Autoencoder [TensorLayer](http://tensorlayercn.readthedocs.io/zh/latest/user/tutorial.html#tensorlayer) [[中文]](http://tensorlayercn.readthedocs.io/zh/latest/user/tutorial.html#denoising-autoencoder) - Convolutional Neural Network [TensorFlow](https://www.tensorflow.org/versions/master/tutorials/mnist/pros/index.html) [[中文]](http://wiki.jikexueyuan.com/project/tensorflow-zh/tutorials/mnist_pros.html) [TensorLayer](http://tensorlayer.readthedocs.io/en/latest/user/tutorial.html#convolutional-neural-network-cnn) [[中文]](http://tensorlayercn.readthedocs.io/zh/latest/user/tutorial.html#convolutional-neural-network) - Recurrent Neural Network [TensorFlow](https://www.tensorflow.org/versions/master/tutorials/recurrent/index.html#recurrent-neural-networks) [[中文]](http://wiki.jikexueyuan.com/project/tensorflow-zh/tutorials/recurrent.html) [TensorLayer](http://tensorlayer.readthedocs.io/en/latest/user/tutorial.html#understand-lstm) [[中文]](http://tensorlayercn.readthedocs.io/zh/latest/user/tutorial.html#lstm) - Deep Reinforcement Learning [TensorLayer](http://tensorlayer.readthedocs.io/en/latest/user/tutorial.html#understand-reinforcement-learning) [[中文]](http://tensorlayercn.readthedocs.io/zh/latest/user/tutorial.html#id13) - Sequence to Sequence [TensorFlow](https://www.tensorflow.org/versions/master/tutorials/seq2seq/index.html#sequence-to-sequence-models) [TensorLayer](http://tensorlayer.readthedocs.io/en/latest/user/tutorial.html#understand-translation)[[中文]](http://tensorlayercn.readthedocs.io/zh/latest/user/tutorial.html#id30) - Word Embedding [TensorFlow](https://www.tensorflow.org/versions/master/tutorials/word2vec/index.html#vector-representations-of-words) [[中文]](http://wiki.jikexueyuan.com/project/tensorflow-zh/tutorials/word2vec.html) [TensorLayer](http://tensorlayer.readthedocs.io/en/latest/user/tutorial.html#understand-word-embedding) [[中文]](http://tensorlayercn.readthedocs.io/zh/latest/user/tutorial.html#word-embedding) ## Deep Learning Reading List - [MIT Deep Learning Book](http://www.deeplearningbook.org) - [Karpathy Blog](http://karpathy.github.io) - [Stanford UFLDL Tutorials](http://deeplearning.stanford.edu/tutorial/) - [Colah's Blog - Word Embedding](http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/) [[中文]](http://dataunion.org/9331.html) - [Colah's Blog - Understand LSTN](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) [[门函数]](http://mp.weixin.qq.com/s?__biz=MzI3NDExNDY3Nw==&mid=2649764821&idx=1&sn=dd325565b40fcbad6e90a9398414dede&scene=2&srcid=0505U2iFJ7tfXgB8yPfNkwrA&from=timeline&isappinstalled=0#wechat_redirect) ## Tutorial index #### 0 - Prerequisite - Introduction to Machine Learning ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/ml_introduction.ipynb)) - Introduction to MNIST Dataset ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb)) #### 1 - Introduction - Hello World ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/helloworld.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/helloworld.py)) - Basic Operations ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_operations.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_operations.py)) #### 2 - Basic Models - Nearest Neighbor ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/nearest_neighbor.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/nearest_neighbor.py)) - Linear Regression ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/linear_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/linear_regression.py)) - Logistic Regression ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/logistic_regression.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/logistic_regression.py)) #### 3 - Neural Networks - Multilayer Perceptron ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/multilayer_perceptron.py)) - Convolutional Neural Network ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/convolutional_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/convolutional_network.py)) - Recurrent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/recurrent_network.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/recurrent_network.py)) - Bidirectional Recurrent Neural Network (LSTM) ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/bidirectional_rnn.py)) - Dynamic Recurrent Neural Network (LSTM) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/dynamic_rnn.py)) - AutoEncoder ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/3_NeuralNetworks/autoencoder.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3_NeuralNetworks/autoencoder.py)) #### 4 - Utilities - Save and Restore a model ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/save_restore_model.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/save_restore_model.py)) - Tensorboard - Graph and loss visualization ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/4_Utils/tensorboard_basic.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_basic.py)) - Tensorboard - Advanced visualization ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/4_Utils/tensorboard_advanced.py)) #### 5 - Multi GPU - Basic Operations on multi-GPU ([notebook](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/5_MultiGPU/multigpu_basics.ipynb)) ([code](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_MultiGPU/multigpu_basics.py)) ## 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](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb). Official Website: [http://yann.lecun.com/exdb/mnist/](http://yann.lecun.com/exdb/mnist/) ## Selected Repositories - [jtoy/awesome-tensorflow](https://github.com/jtoy/awesome-tensorflow) - [nlintz/TensorFlow-Tutoirals](https://github.com/nlintz/TensorFlow-Tutorials) - [adatao/tensorspark](https://github.com/adatao/tensorspark) - [ry/tensorflow-resnet](https://github.com/ry/tensorflow-resnet) ## Examples ### Basics - Multi-layer perceptron (MNIST). A multi-layer perceptron implementation for MNIST classification task, see ``tutorial_mnist_simple.py`` [here](https://github.com/zsdonghao/tensorlayer). ### Computer Vision - Denoising Autoencoder (MNIST). A multi-layer perceptron implementation for MNIST classification task, see ``tutorial_mnist.py`` [here](https://github.com/zsdonghao/tensorlayer). - Stacked Denoising Autoencoder and Fine-Tuning (MNIST). A multi-layer perceptron implementation for MNIST classification task, see ``tutorial_mnist.py`` [here](https://github.com/zsdonghao/tensorlayer). - Convolutional Network (MNIST). A Convolutional neural network implementation for classifying MNIST dataset, see ``tutorial_mnist.py`` [here](https://github.com/zsdonghao/tensorlayer). - Convolutional Network (CIFAR-10). A Convolutional neural network implementation for classifying CIFAR-10 dataset, see ``tutorial_cifar10.py`` [here](https://github.com/zsdonghao/tensorlayer). - VGG 16 (ImageNet). A Convolutional neural network implementation for classifying ImageNet dataset, see ``tutorial_vgg16.py`` [here](https://github.com/zsdonghao/tensorlayer). - VGG 19 (ImageNet). A Convolutional neural network implementation for classifying ImageNet dataset, see ``tutorial_vgg19.py`` [here](https://github.com/zsdonghao/tensorlayer). ### Natural Language Processing - Recurrent Neural Network (LSTM). Apply multiple LSTM to PTB dataset for language modeling, see ``tutorial_ptb_lstm.py`` [here](https://github.com/zsdonghao/tensorlayer). - Word Embedding - Word2vec. Train a word embedding matrix, see ``tutorial_word2vec_basic.py`` [here](https://github.com/zsdonghao/tensorlayer). - Restore Embedding matrix. Restore a pre-train embedding matrix, see ``tutorial_generate_text.py`` [here](https://github.com/zsdonghao/tensorlayer). - Text Generation. Generates new text scripts, using LSTM network, see ``tutorial_generate_text.py`` [here](https://github.com/zsdonghao/tensorlayer). - Machine Translation (WMT). Translate English to French. Apply Attention mechanism and Seq2seq to WMT English-to-French translation data, see ``tutorial_translate.py`` [here](https://github.com/zsdonghao/tensorlayer). ### Reinforcement Learning - Deep Reinforcement Learning - Pong Game. Teach a machine to play Pong games, see ``tutorial_atari_pong.py`` [here](https://github.com/zsdonghao/tensorlayer).