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UPDATE (July 12, 2016)

New free MOOC course covering all of this material in much more depth, as well as much more including combined variational autoencoders + generative adversarial networks, visualizing gradients, deep dream, style net, and recurrent networks: https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow-i/info

TensorFlow Tutorials

You can find python source code under the python directory, and associated notebooks under notebooks.

Source code Description
1 basics.py Setup with tensorflow and graph computation.
2 linear_regression.py Performing regression with a single factor and bias.
3 polynomial_regression.py Performing regression using polynomial factors.
4 logistic_regression.py Performing logistic regression using a single layer neural network.
5 basic_convnet.py Building a deep convolutional neural network.
6 modern_convnet.py Building a deep convolutional neural network with batch normalization and leaky rectifiers.
7 autoencoder.py Building a deep autoencoder with tied weights.
8 denoising_autoencoder.py Building a deep denoising autoencoder which corrupts the input.
9 convolutional_autoencoder.py Building a deep convolutional autoencoder.
10 residual_network.py Building a deep residual network.
11 variational_autoencoder.py Building an autoencoder with a variational encoding.

Installation Guides

For Ubuntu users using python3.4+ w/ CUDA 7.5 and cuDNN 7.0, you can find compiled wheels under the wheels directory. Use pip3 install tensorflow-0.8.0rc0-py3-none-any.whl to install, e.g. and be sure to add: export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64" to your .bashrc. Note, this still requires you to install CUDA 7.5 and cuDNN 7.0 under /usr/local/cuda.

Resources

Author

Parag K. Mital, Jan. 2016.

http://pkmital.com

License

See LICENSE.md

Latest Releases
1.0.0
 Feb. 2 2016