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Neural Networks

Neural networks can be thought of as a robust approach to approximating real- valued, discrete-values, and vector-valued target functions.

www.cis.upenn.edu·2.1 MB
Introduction to Neural Networks

Many Applications of Neural Networks Used in unsupervised, supervised, and reinforcement learning Focus on use for supervised learning here Not a different type of learning – just a different type of function

courses.cs.duke.edu·902.2 KB
Neural Networks - Stanford University

I a neural network (NN) is a nonlinear predictor ^y = g ( x ) with a particular layered form NNs can be thought of as incorporating aspects of feature engineering into the predictor (and indeed are often used as ‘automatic feature engineering’) the parameter dimension p can be very large training NNs can be tricky, and take a long time NNs can perf...

ee104.stanford.edu·749.7 KB
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