Gradient Descent, Stochastic Optimization, And Other Tales
Language: English
Published by Eliva Press, 2022
- Softcover
- New

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- Title
- Gradient Descent, Stochastic Optimization, And Other Tales
- Author
- Lu, Jun
- Publisher
- Eliva Press
- Publication year
- 2022
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 9994981552
- ISBN 13
- 9789994981557
Gradient descent is one of the most popular algorithms to perform optimization and by far the most common way to optimize machine learning tasks. Its stochastic version receives attention in recent years, and this is particularly true for optimizing deep neural networks. In deep neural networks, the gradient followed by a single sample or a batch of samples is employed to save computational resources and escape from saddle points. In 1951, Robbins and Monro published A stochastic approximation method, one of the first modern treatments on stochastic optimization that estimates local gradients with a new batch of samples. And now, stochastic optimization has become a core technology in machine learning, largely due to the development of the back propagation algorithm in fitting a neural network. The sole aim of this article is to give a self-contained introduction to concepts and mathematical tools in gradient descent and stochastic optimization.
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