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From the basics to slightly more interesting applications of Tensorflow

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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:

TensorFlow Tutorials

You can find python source code under the

directory, and associated notebooks under

| | Source code | Description | | --- | --- | --- | |1| | Setup with tensorflow and graph computation.| |2| | Performing regression with a single factor and bias. | |3| | Performing regression using polynomial factors.| |4| | Performing logistic regression using a single layer neural network.| |5| | Building a deep convolutional neural network.| |6| | Building a deep convolutional neural network with batch normalization and leaky rectifiers.| |7| | Building a deep autoencoder with tied weights.| |8| | Building a deep denoising autoencoder which corrupts the input.| |9| | Building a deep convolutional autoencoder.| |10| | Building a deep residual network.| |11| | 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

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
. Note, this still requires you to install CUDA 7.5 and cuDNN 7.0 under



Parag K. Mital, Jan. 2016.



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