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Andrew Ng: #DeepLearning, Self-Taught Learning and Unsupervised Feature Learning

Machine learning is a very successful technology but applying it today often requires spending substantial effort hand-designing features. This is true for applications in vision, audio and text. To address this, Ng's group and others are working on "deep learning" algorithms, which can automatically learn feature representations (often from unlabeled data) thus avoiding a lot of time-consuming engineering. These algorithms are based on building massive artificial neural networks that were loosely inspired by cortical (brain) computations. As part of this work, Ng also founded and formerly led a project at Google to build massive deep learning algorithms. This work resulted in a highly distributed neural network with over 1 billion parameters trained on 16,000 CPU cores that learned by itself to discover high level concepts -- such as "cats" -- from watching unlabeled YouTube videos.

Description: This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. By working through it, you will also get to implement several feature learning/deep learning algorithms, get to see them work for yourself, and learn how to apply/adapt these ideas to new problems.

This tutorial assumes a basic knowledge of machine learning (specifically, familiarity with the ideas of supervised learning, logistic regression, gradient descent). If you are not familiar with these ideas, we suggest you go to this Machine Learning course and complete sections II, III, IV (up to Logistic Regression) first.

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