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First-Order and Stochastic Optimization Methods for Machine Learning: Algorithms and Applications
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- A comprehensive guide to first-order and stochastic optimization methods for machine learning, covering both algorithms and applications.
- Provides a unified framework for understanding and comparing different optimization methods.
- Includes detailed explanations of the theoretical foundations of each method, as well as practical guidance on their implementation.
- Covers a wide range of applications, including supervised learning, unsupervised learning, and reinforcement learning.
- Written by leading experts in the field, this book is an essential resource for researchers and practitioners alike.
Condition: New original from publisher
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