From the basics to slightly more interesting applications of Tensorflow
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01_basics.ipynb: update tensorflow methods
Latest commit 376ade7 Sep 18, 2018

README.md

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