Deep Learning from Scratch: Building with Python from First Principles
Language: English
Published by O'Reilly Media, 2019
- Softcover
- New
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
AbeBooks seller since March 25, 2015
Condition: New
US$ 63.52
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In English.
Seller Inventory # ria9781492041412_new
- Title
- Deep Learning from Scratch: Building with Python from First Principles
- Author
- Weidman, Seth
- Publisher
- O'Reilly Media
- Publication year
- 2019
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1492041416
- ISBN 13
- 9781492041412
- Item weight
- 574 grams
With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. Youâ??ll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way.
Author Seth Weidman shows you how neural networks work using a first principles approach. Youâ??ll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, youâ??ll be set up for success on all future deep learning projects.
This book provides:
- Extremely clear and thorough mental modelsâ??accompanied by working code examples and mathematical explanationsâ??for understanding neural networks
- Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework
- Working implementations and clear-cut explanations of convolutional and recurrent neural networks
- Implementation of these neural network concepts using the popular PyTorch framework
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Ria Christie Collections
Uxbridge, United Kingdom
AbeBooks seller since March 25, 2015
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