kylepjohnson/tensorflow_tutorials

From the basics to slightly more interesting applications of Tensorflow

TensorFlow Tutorials

Install tensorflow using the instructions here: https://github.com/tensorflow/tensorflow https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get_started/os_setup.md

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 ...

Author

Parag K. Mital, Jan. 2016. http://pkmital.com

License

See LICENSE.md