From the basics to slightly more interesting applications of Tensorflow
Jupyter Notebook
5,672
75 commits
updated Dec 11, 2021
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. |
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="$LD_LIBRARY_PATH:/usr/local/cuda/lib64" to your .bashrc. Note, this still requires you to install CUDA 7.5 and cuDNN 7.0 under /usr/local/cuda.
Parag K. Mital, Jan. 2016.
See LICENSE.md
225,402 followers · starred Feb 2016
1,558 followers · starred Dec 2017
27 followers · starred Feb 2016
85 followers · starred Feb 2016
From the basics to slightly more interesting applications of Tensorflow
Jupyter Notebook
5,672
75 commits
updated Dec 11, 2021
You can find python source code under the python directory, and associated notebooks under notebooks.
| Source code | Description | |
|---|---|---|
| 1 | basics.py | Setup with tensorflow and graph computation. |
| 2 | linear_regression.py | Performing regression with a single factor and bias. |
| 3 | polynomial_regression.py | Performing regression using polynomial factors. |
| 4 | logistic_regression.py | Performing logistic regression using a single layer neural network. |
| 5 | basic_convnet.py | Building a deep convolutional neural network. |
| 6 | modern_convnet.py | Building a deep convolutional neural network with batch normalization and leaky rectifiers. |
| 7 | autoencoder.py | Building a deep autoencoder with tied weights. |
| 8 | denoising_autoencoder.py | Building a deep denoising autoencoder which corrupts the input. |
| 9 | convolutional_autoencoder.py | Building a deep convolutional autoencoder. |
| 10 | residual_network.py | Building a deep residual network. |
| 11 | variational_autoencoder.py | Building an autoencoder with a variational encoding. |
For Ubuntu users using python3.4+ w/ CUDA 7.5 and cuDNN 7.0, you can find compiled wheels under the wheels directory. Use pip3 install tensorflow-0.8.0rc0-py3-none-any.whl to install, e.g. and be sure to add: export LD_LIBRARY_PATH="$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.
Parag K. Mital, Jan. 2016.
See LICENSE.md
225,402 followers · starred Feb 2016
1,558 followers · starred Dec 2017
27 followers · starred Feb 2016
85 followers · starred Feb 2016
