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About Chapter 6 - Feedforward Deep Networks
This document discusses feedforward deep neural networks. It begins by introducing the basic architecture of a multilayer perceptron (MLP) with one hidden layer for regression problems. MLPs can learn powerful nonlinear transformations through their hidden layers. The document then discusses generalizing this framework to define parametrized families of functions, loss functions, regularization, and optimization procedures for training deep networks. It provides an example of applying this framework to a vanilla MLP for regression. Finally, it discusses various activation functions that can be used in deep networks, such as rectified linear units, softmax, and maxout units.
Detailed Information
| Author: | ['justdls'] |
|---|---|
| Publication Year: | 2015 |
| Pages: | 27 |
| Language: | English |
| Format: | |
| Price: | FREE |
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