Class meeting for 10-405 Deep Learning

From Cohen Courses
Jump to: navigation, search

This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-405 in Spring 2018.

Contents

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)