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About FTML Book
This document discusses learning deep architectures for artificial intelligence. Deep architectures are composed of multiple levels of non-linear operations, like neural networks with many hidden layers. Learning algorithms like deep belief networks have been proposed to learn the parameters of deep architectures. The document outlines challenges in training deep neural networks and reviews approaches like unsupervised pre-training of layers and joint training using variational bounds. The goal is for deep learning algorithms to discover complex hierarchical representations needed for intelligence-level tasks like computer vision and natural language understanding.
Detailed Information
| Author: | ['Jorge'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 130 |
| Language: | English |
| Format: | |
| Price: | FREE |
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