Introduction to neural networks

CS-449: Neural Networks Fall 99 Instructor: Genevieve Orr Willamette University Lecture Notes prepared by Genevieve Orr, Nici Schraudolph, and Fred Cummins [Content][Links] Course content Summary Our goal is to introduce students to a powerful class of model, the Neural Network. We then introduce one kind of network in detail: the feedforward network trained by backpropagation of error. Lecture 1: Introduction Lecture 2: Classification Lecture 3: Optimizing Linear Networks Lecture 4: The Backprop Toolbox Lecture 5: Unsupervised Learning Lecture 6: Reinforcement Learning Lecture 7: Advanced Topics [Top] Review for Midterm: Links Tutorials: The Nervous System - a very nice introduction, many pictures Neural Java - a neural network tutorial with Java applets Web Sim - A Java neural network simulator. a book chapter describing the Backpropagation Algorithm (Postscript) A short set of pages showing how a simple backprop net learns to recognize the digits 0-9, with C code Reinforcement Learning - A Tutorial

brains in silicon Welcome to Brains in Silicon. Learn about the lab, get to know the brains that work here, and find out about new projects that you could join. We have crafted two complementary objectives: To use existing knowledge of brain function in designing an affordable supercomputer—one that can itself serve as a tool to investigate brain function—feeding back and contributing to a fundamental, biological understanding of how the brain works. We model brains using an approach far more efficient than software simulation: We emulate the flow of ions directly with the flow of electrons—don't worry, on the outside it looks just like software. Welcome and enjoy your time here!