neural networks ann nn notes free ppt

Lecture 1: Introduction

• Questions
• Motivation and Applications
• Computation in the brain
• Artificial neuron models
• Linear regression
• Linear neural networks
• Multi-layer networks
• Error Backpropagation

Lecture 2: Classification

• Introduction
• Perceptron Learning
• Delta Learning
• Doing it Right

Lecture 3: Optimizing Linear Networks

• Weights and Learning Rates
• Summary

Lecture 4: The Backprop Toolbox

• 2-Layer Networks and Backprop
• Noise and Overtraining
• Momentum
• Delta-Bar-Delta
• Many layer Networks and Backprop
• Backprop: an example
• Overfitting and regularization
• Growing and pruning networks
• Preconditioning the network
• Momentum
• Delta-Bar-Delta

Lecture 5: Unsupervised Learning

• Introduction
• Linear Compression (PCA)
• NonLinear Compression
• Competitive Learning
• Kohonon Self-Organizing Nets

Lecture 6: Reinforcement Learning

• Introduction
• Components of RL
• Terminology and Bellman's Equation

Lecture 7: Advanced Topics

• Learning rate adaptation
• Classification
• Non-supervised learning
• Time-Delay Neural Networks
• Recurrent neural networks
• Real-Time Recurrent Learning
• Dynamics of RNNs
• Long Short-Term Memory