Neural Nets. 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.
Neural network. An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain.
Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another. For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated. This determines which character was read. Like other machine learning methods - systems that learn from data - neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition. Background History Farley and Wesley A. Artificial neural network.
Artificial Intelligence. Can intelligence be artificial. I am sure the question concerns computers, and kind of half serious answers like 'I make my intelligence therefore its artificial' are not only not helpful but they probably say more about people in AI trying to avoid the poor track record of AI as a domain. The answer is we don't know yet. We an never prove a negative so we have only two possible answer: yes someone made a machine that is clearly intelligent say like a 6 year old, or we don't know. Right now we are stuck at 'we don't know', and we have been here so long we are progressed a bit to 'and boy it would be hard if possible'.
This from say 60 years ago thinking that it would probably be a matter of time. There is a philosophy thought experiment to try and prove strong AI is impossible called the China Room, which is so well known I won't go in to details here. But I think most people now accept that approaches from neural nets to case based deduction to rule systems will not produce intelligence.
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