Neural Networks for Machine Learning

CtrlK

Lec 01: Introduction
Lec 02: The Perceptron Learning Procedure
Lec 03: The Backpropagation Learning Procedure
Lec 04: Learning Feature - Vectors for Words
Lec 05: Object Recognition with Neural Nets
Lec 06: Optimization - How to Make the Learning Go Faster
Lec 07: Recurrent Neural Network Part 1
Lec 08: Recurrent Neural Network Part 2
Lec 09: Ways to MAke Neural Network Generalize Better
Lec 10: Combining Multiple Neural Network to Improve Generalization
Lec 11: Hopfield Nets & Boltzman Machine
Lec 12: Restricted Boltzman Machine
Lec 13: Staking RBMs to Make Deep Belief Nets
Lec 14: Deep Neural Nets with Generative Pre Training
Lec 15: Modeling Hierarchical Structure with Neural nets
Lec 16: Recent Applications of Deep Nets

Powered by GitBook

On this page

Lec 11: Hopfield Nets & Boltzman Machine

PreviousLec 10: Combining Multiple Neural Network to Improve Generalization NextLec 12: Restricted Boltzman Machine