TensorFlow Tutorials
1-basics.py
Basics of getting setup w/ 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 w/ batch normalization and leaky rectifiers.
7-autoencoder.py
Building a deep autoencoder w/ 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.
... More to come ...
Installation Guides
Resources
Author
Parag K. Mital, Jan. 2016. http://pkmital.com
License
See LICENSE.md