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About Optimization For Deep Learning Theory and Algorithms
This document provides an overview of optimization algorithms and theory for training neural networks. It discusses issues like gradient explosion/vanishing and proposes solutions such as careful initialization and normalization methods. It also reviews commonly used optimization methods for training neural networks like SGD, adaptive gradient methods, and distributed methods. Additionally, it covers existing research on the global properties of neural network training, such as results on local minima and infinite-width analysis.
Detailed Information
| Author: | ['hunternamkhung'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 60 |
| Language: | English |
| Format: | |
| Price: | FREE |
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