https://github.com/pkmital/tensorflow_tutorials

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

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From the basics to slightly more interesting applications of Tensorflow

• Host: GitHub
• URL: https://github.com/pkmital/tensorflow_tutorials
• Owner: pkmital
• License: other
• Created: 2016-02-03T04:01:40.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2021-12-11T20:54:51.000Z (about 3 years ago)
• Last Synced: 2024-12-05T15:05:05.602Z (8 days ago)
• Language: Jupyter Notebook
• Size: 122 MB
• Stars: 5,637
• Watchers: 239
• Forks: 1,178
• Open Issues: 10
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# TensorFlow Tutorials

You can find python source code under the `python` directory, and associated notebooks under `notebooks`.

| | Source code | Description |

| --- | --- | --- |

|1| **[basics.py](python/01_basics.py)** | Setup with tensorflow and graph computation.|

|2| **[linear_regression.py](python/02_linear_regression.py)** | Performing regression with a single factor and bias. |

|3| **[polynomial_regression.py](python/03_polynomial_regression.py)** | Performing regression using polynomial factors.|

|4| **[logistic_regression.py](python/04_logistic_regression.py)** | Performing logistic regression using a single layer neural network.|

|5| **[basic_convnet.py](python/05_basic_convnet.py)** | Building a deep convolutional neural network.|

|6| **[modern_convnet.py](python/06_modern_convnet.py)** | Building a deep convolutional neural network with batch normalization and leaky rectifiers.|

|7| **[autoencoder.py](python/07_autoencoder.py)** | Building a deep autoencoder with tied weights.|

|8| **[denoising_autoencoder.py](python/08_denoising_autoencoder.py)** | Building a deep denoising autoencoder which corrupts the input.|

|9| **[convolutional_autoencoder.py](python/09_convolutional_autoencoder.py)** | Building a deep convolutional autoencoder.|

|10| **[residual_network.py](python/10_residual_network.py)** | Building a deep residual network.|

|11| **[variational_autoencoder.py](python/11_variational_autoencoder.py)** | Building an autoencoder with a variational encoding.|

# Installation Guides

* [TensorFlow Installation](https://github.com/tensorflow/tensorflow)

* [OS specific setup](https://github.com/tensorflow/tensorFlow/blob/master/tensorflow/g3doc/get_started/os_setup.md)

* [Installation on EC2 GPU Instances](http://eatcodeplay.com/installing-gpu-enabled-tensorflow-with-python-3-4-in-ec2/)

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

* [Official Tensorflow Tutorials](https://www.tensorflow.org/versions/r0.7/tutorials/index.html)

* [Tensorflow API](https://www.tensorflow.org/versions/r0.7/api_docs/python/index.html)

* [Tensorflow Google Groups](https://groups.google.com/a/tensorflow.org/forum/#!forum/discuss)

* [More Tutorials](https://github.com/nlintz/TensorFlow-Tutorials)

# Author

Parag K. Mital, Jan. 2016.

http://pkmital.com

# License

See LICENSE.md