Unsupervised Feature Learning and Deep Learning

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Logo Stanford University Open Classroom

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Description

Course Description

Machine learning has seen numerous successes, but applying learning algorithms today often means spending a long time hand-engineering the input feature representation. This is true for many problems in vision, audio, NLP, robotics, and other areas. In this course, you'll learn about methods for unsupervised feature learning and deep learning, which automatically learn a good representation of the input from unlabeled data. You'll also pick up the "hands-on," practical skills and tricks-of-the-trade needed to get these algorithms to work well.

Basic knowledge of machine learning (supervised learning) is assumed, though we'll quickly review logistic regression and gradient …

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Course Description

Machine learning has seen numerous successes, but applying learning algorithms today often means spending a long time hand-engineering the input feature representation. This is true for many problems in vision, audio, NLP, robotics, and other areas. In this course, you'll learn about methods for unsupervised feature learning and deep learning, which automatically learn a good representation of the input from unlabeled data. You'll also pick up the "hands-on," practical skills and tricks-of-the-trade needed to get these algorithms to work well.

Basic knowledge of machine learning (supervised learning) is assumed, though we'll quickly review logistic regression and gradient descent.


I. INTRODUCTION



II. LOGISTIC REGRESSION


  • Representation(1.5x)
  • Batch gradient descent(1.2x)(1.5x)
  • Gradient descent in practice(1.2x)(1.5x)
  • Stochastic gradient descent
  • Exponentially weighted average
  • Shuffling data
  • Exercise 1: Implementation

III. NEURAL NETWORKS


  • Representation
  • Architecture
  • Examples and intuitions #1(1.2x)
  • Examples and intuitions #2
  • Parameter learning
  • Gradient checking
  • Random initialization
  • Vectorized implementation
  • Activation function derivative

V. APPLICATION TO CLASSIFICATION



IV. UNSUPERVISED FEATURE LEARNING and SELF-TAUGHT LEARNING



V. APPLICATION TO CLASSIFICATION



VI. DEEP LEARNING WITH AUTOENCODERS



VII. SPARSE REPRESENTATIONS



VIII. WHITENING



IX. INDEPENDENT COMPONENTS ANALYSIS (ICA)



X. SLOW FEATURE ANALYSIS (SFA)



XI. RESTRICTED BOLTZMANN MACHINES (RBM)



XII. DEEP BELIEF NETWORKS (DBN)



Teacher: Andrew Ng

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