McGraw-Hill, 1991 - 621 páginas
The breadth of A. I. is explored and explained in this best selling text. Assuming no prior knowledge,it covers topics like neural networks and robotics. This text explores the range of problems which have been and remain to be solved using A. I. tools and techniques. The second half of this text is an excellent reference.
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Let's now return to backpropagation networks. The unit in a backpropagation
network requires a slightly different activation function from the perceptron. Both
functions are shown in Figure 18.16. A backpropagation unit still sums up its ...
Like a perceptron, a backpropagation network typically starts out with a random
set of weights. The network adjusts its weights each time it sees an input-output
pair. Each pair requires two stages: a forward pass and a backward pass.
Recurrent networks can be trained with the backpropagation algorithm. At each
step, we compare the activations of the output units with the desired activations
and propagate errors backward through the network. When training is completed,
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very poor book for novice.Assumes u already know AI and the language is not at all user friendly.One has to read a sentence repeatedly to get a grasp and some times even then u don't understand.Go for other books much better than this
not soo good to go for novice
What Is Artificial Intelligence?
Problems Problem Spaces and Search
Heuristic Search Techniques
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