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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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CSE 599W: Systems for ML by Tianqi Chen, Haichen Shen, and Arvind Krishnamurthy at University of Washington.
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Deep Learning Specialization by deeplearning.ai on Coursera.
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AI For Everyone by deeplearning.ai on Coursera.
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CS294-158 Deep Unsupervised Learning by Pieter Abbeel at University of California, Berkeley.
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COMS W4995 Applied Machine Learning by Andreas C. Müller at Columbia University.
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计算导论与C语言基础 by Peking University on Coursera.
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CS224n: Natural Language Processing with Deep Learning by Christopher Manning at Stanford University.
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Launching into Machine Learning by Google Cloud on Coursera.
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How Google does Machine Learning by Google Cloud on Coursera.
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Intro to TensorFlow by Google Cloud on Coursera.
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The Data Scientist’s Toolbox by Johns Hopkins University on Coursera.
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Introduction to Structured Query Language (SQL) by University of Michigan on Coursera.
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CS231n: Convolutional Neural Networks for Visual Recognition by Fei-Fei Li at Stanford University.
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CS 20: Tensorflow for Deep Learning Research by Chip Huyen at Stanford University.
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Machine Learning by Hung-yi Lee at National Taiwan University.
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Gradient Descent with Momentum
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Batch Norm at train time
Short description of portfolio item number 1
Short description of portfolio item number 2
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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.