GitHub - pkmital/tensorflow_tutorials: From the basics to slightly more interesting applications of Tensorflow

Abstraction: TensorFlow tutorial series from basics through autoencoders and residual networks

Key points:

• 11-tutorial series by Parag K. Mital (Jan. 2016) progressing from TensorFlow graph basics through advanced architectures
• Covers linear regression, polynomial regression, and logistic regression as single-layer neural network introductions
• Implements convolutional networks (basic and modern with batch normalization and leaky rectifiers), plus residual networks
• Autoencoder series includes standard deep autoencoder with tied weights, denoising autoencoder, convolutional autoencoder, and variational autoencoder
• Code available as both Python source files and Jupyter notebooks; pre-compiled wheels for Ubuntu Python 3.4+ with CUDA 7.5 and cuDNN 7.0
• Requires CUDA 7.5 and cuDNN 7.0 under /usr/local/cuda for GPU support

Connections: Tensorflow · Github · Deep Learning · Neural Networks · Autoencoders

Source: https://github.com/pkmital/tensorflow_tutorials