Deep Learning Tutorials with Tensorflow

The deeplearning algorithms are carefully implemented by tensorflow.

Environment

• Python 3.5
• tensorflow 0.12

The deeplearning algorithms includes (now):

• Logistic Regression logisticRegression.py
• Multi-Layer Perceptron (MLP) mlp.py
• Convolution Neural Network (CNN) cnn.py
• Denoising Aotoencoder (DA) da.py
• Stacked Denoising Autoencoder (SDA) sda.py
• Restricted Boltzmann Machine (RBM) [rbm.py gbrbm.py]
• Deep Belief Network (DBN) dbn.py

Note: the project aims at imitating the well-implemented algorithms in Deep Learning Tutorials (coded by Theano).

Practical examples

You can find more practical examples with tensorflow here:

• CNN for setence classification [self] [blog] [paper]
• RNN for language model [self] [blog] [blog_cn]
• LSTM for language model (PTB data) [self] [tutorial] [paper]
• VGG model for image classification (object recongnition) [self] [source]
• Residual network for cifar10_dataset [self] [source] [paper]
• LSTM for time series prediction [self] [source]
• Generative adversarial network (GAN) [self]
• Variational autoencoder (VAE) [self]

Results

Fun Blogs

• Chatbots with Seq2Seq

Personal Notes

• Tensorflow for RNNs [tf_rnn.ipynb]
• Tensorflow for Autoencoder [tf_autoencoder.ipynb]

Other Tutorials

• ageron/handson-ml
• Hvass-Labs/TensorFlow-Tutorials
• BinRoot/TensorFlow-Book
• sjchoi86/dl_tutorials_10weeks

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