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About Optimization in Machine Learning
The document compares optimization algorithms for deep learning. It discusses how adaptive gradient methods have become widely used as neural networks have increased in depth and size. The paper examines commonly used adaptive optimization algorithms in detail and implements them for supervised and unsupervised tasks using image datasets. It analyzes the behavior of the algorithms during training and their performance results, pointing out differences from basic algorithms like stochastic gradient descent.
Detailed Information
| Author: | ['rbernalMx'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 26 |
| Language: | English |
| Format: | |
| Price: | FREE |
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