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About Deep Learning & Neural Networks: Kevin Duh
This document summarizes a lecture on deep learning and neural networks. It discusses the motivation for deep architectures to model high-level abstractions with highly non-linear functions. A key breakthrough in 2006 was layer-wise pre-training, where layers are trained sequentially to model the input distribution before fine-tuning for the task. Two main approaches are covered: Deep Belief Nets using Restricted Boltzmann Machines and Stacked Autoencoders. Training Restricted Boltzmann Machines optimizes the data likelihood by matching model expectations to data expectations.
Detailed Information
| Author: | ['Spirit'] |
|---|---|
| Publication Year: | 2015 |
| Pages: | 86 |
| Language: | English |
| Format: | |
| Price: | FREE |
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