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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Create a perceptron with n + 1 inputs and n + 1 weights , where the extra input xo
is always set to 1 . 2 . Initialize the weights ( wo , W1 , . . . , wn ) to random real
values . 3 . Iterate through the training set , collecting all examples misclassified
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 .
Produce : A set of weights such that the output units become active according to
some natural division of the inputs . 1 . Present an input vector , denoted ( X1 , X2
, . . . , xn ) . 2 . Calculate the initial activation for each output unit by computing a ...
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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
Heuristic Search Techniques
Knowledge Representation Issues
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