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About Neural Networks / Deep Learning
This document provides an overview of neural networks and deep learning. It discusses neural network models for both regression and classification problems. The key points covered are: - Neural networks learn nonlinear models by extracting features from raw input data and learning linear models on top of the extracted features. - A basic neural network model uses one hidden layer of units to extract nonlinear features from the input, which are then linearly combined to produce an output. - Popular activation functions for hidden units include the logistic sigmoid, tanh, and rectified linear unit (ReLU). - Neural networks can be extended to multiple hidden layers, with each layer learning higher-level features from the previous layer. - Model parameters consist of weights
Detailed Information
| Author: | ['Yiwei Chen'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 9 |
| Language: | English |
| Format: | |
| Price: | FREE |
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