Showing content from https://github.com/pkmital/tensorflow_tutorials below:
pkmital/tensorflow_tutorials: From the basics to slightly more interesting applications of Tensorflow
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
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