Difference between revisions of "Class meeting for 10-605 Deep Learning"

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This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2016|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall_2016]].
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This is one of the class meetings on the [[Syllabus for Machine Learning with Large Datasets 10-605 in Fall 2017|schedule]] for the course [[Machine Learning with Large Datasets 10-605 in Fall_2017]].
  
 
=== Slides ===
 
=== Slides ===

Revision as of 13:54, 23 October 2017

This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall_2017.

Slides

Quizzes

Sample code

Readings

Things to remember

  • The underlying reasons deep networks are hard to train
  • Exploding/vanishing gradients
  • Saturation
  • The importance of key recent advances in neural networks:
  • Matrix operations and GPU training
  • ReLU, cross-entropy, softmax
  • How backprop can be generalized to a sequence of assignment operations (autodiff)
    • Wengert lists
    • How to evaluate and differentiate a Wengert list
  • Common architectures
    • Multi-layer perceptron
    • Recursive NNs (RNNS) and Long/short term memory networks (LSTMs)
    • Convolutional Networks (CNNs)