Math and Architectures of Deep Leaing

by: Krishnendu Chaudhury (Author)

Publisher: Manning Publications
Edition:1st

Publication Date: 15 Mar. 2024

Language: English

Print Length: 450 pages

ISBN-10: 1617296481

ISBN-13: 9781617296482

Book Description

The mathematical paradigms that underlie deep leaing typically start out as hard-to-read academic papers, often leaving engineers in the dark about how their models actually function. Math and Architectures of Deep Leaingbridges the gap between theory and practice, laying out the math of deep leaing side by side with practical implementations in Python and PyTorch. Written by deep leaing expert Krishnendu Chaudhury, you’ll peer inside the “black box” to understand how your code is working, and lea to comprehend cutting-edge research you can tu into practical applications. about the technologyIt’s important to understand how your deep leaing models work, both so that you can maintain them efficiently and explain them to other stakeholders. Leaing mathematical foundations and neural network architecture can be challenging, but the payoff is big. You’ll be free from blind reliance on pre-packaged DL models and able to build, customize, and re-architect for your specific needs. And when things go wrong, you’ll be glad you can quickly identify and fix problems. about the book Math and Architectures of Deep Leaingsets out the foundations of DL in a way that’s both useful and accessible to working practitioners. Each chapter explores a new fundamental DL concept or architectural patte, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. You’ll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research. By the time you’re done, you’ll have a combined theoretical insight and practical skills to identify and implement DL architecture for almost any real-world challenge.
The mathematical paradigms that underlie deep leaing typically start out as hard-to-read academic papers, often leaving engineers in the dark about how their models actually function. Math and Architectures of Deep Leaingbridges the gap between theory and practice, laying out the math of deep leaing side by side with practical implementations in Python and PyTorch. Written by deep leaing expert Krishnendu Chaudhury, you’ll peer inside the “black box” to understand how your code is working, and lea to comprehend cutting-edge research you can tu into practical applications. about the technologyIt’s important to understand how your deep leaing models work, both so that you can maintain them efficiently and explain them to other stakeholders. Leaing mathematical foundations and neural network architecture can be challenging, but the payoff is big. You’ll be free from blind reliance on pre-packaged DL models and able to build, customize, and re-architect for your specific needs. And when things go wrong, you’ll be glad you can quickly identify and fix problems. about the book Math and Architectures of Deep Leaingsets out the foundations of DL in a way that’s both useful and accessible to working practitioners. Each chapter explores a new fundamental DL concept or architectural patte, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. You’ll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research. By the time you’re done, you’ll have a combined theoretical insight and practical skills to identify and implement DL architecture for almost any real-world challenge.

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