# 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.

# License

See LICENSE.md