Machine Learning - complete course notes Home Stanford Machine Learning The following notes represent a complete, stand alone interpretation of Stanford's machine learning course presented by Professor Andrew Ng and originally posted on the website during the fall 2011 semester. The topics covered are shown below, although for a more detailed summary see lecture 19. The only content not covered here is the Octave/MATLAB programming. »

Setting up a Deep Learning Machine from Scratch (Software) A detailed guide to setting up your machine for deep learning research. Includes instructions to install drivers, tools and various deep learning frameworks. This was tested on a 64 bit machine with Nvidia Titan X, running Ubuntu 14.04 There are several great guides with a similar goal. Some are limited in scope, while others are not up to date. This guide is based on (with some portions copied verbatim from): »

The following post describes how to install TensorFlow 0.6 on an Amazon EC2 Instance with GPU Support. I also created a Public AMI (ami-e191b38b) with the resulting setup. Feel free to use it. UPDATED (28 Jan 2016): The latest TensorFlow build requires Bazel 0.1.4. Post now reflects this. Thanks to Jim Simpson for his assistance. UPDATED (28 Jan 2016): The AMI provided now exports env variables in ~/.bashrc. The following things are installed: »

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