Stanford CS 224N | Natural Language Processing with Deep Learning
Prerequisites Proficiency in Python All class assignments will be in Python (using NumPy and PyTorch). If you need to remind yourself of Python, or you're not very familiar with NumPy, you can come to the Python review session in week 1 (listed in the schedule). If you have a lot of programming experience but in a different language (e.g. C/C++/Matlab/Java/Javascript), you will probably be fine. C
TensorFlow Tutorial (Sherry Moore, Google Brain)
The talks at the Deep Learning School on September 24/25, 2016 were amazing. I clipped out individual talks from the full live streams and provided links to each below in case that's useful for people who want to watch specific talks several times (like I do). Please check out the official website (http://www.bayareadlschool.org) and full live streams below. Having read, watched, and presented d
TensorFrames: Deep Learning with TensorFlow on Apache Spark (Tim Hunter)
Since the creation of Apache Spark, I/O throughput has increased at a faster pace than processing speed. In a lot of big data applications, the bottleneck is increasingly the CPU. With the release of Apache Spark 2.0 and Project Tungsten, Spark runs a number of control operations close to the metal. At the same time, there has been a surge of interest in using GPUs (the Graphics Processing Units o
Jeff Dean Talks Google Brain and Brain Residency
Watch Google's Jeff Dean talk about Google Brain and the Brain Residency Program. Jeff will will discuss the current state of the Google Brain Team, Tensorflow, the future directions of Brain and tell you a bit more about our Google Brain Residency Program (2017 application open now! https://goo.gl/Z4TtnQ). Check out g.co/brain for more info! The first part of the video gives an overview of the wo
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Large Scale Deep Learning with TensorFlow
