Math and Architectures of Deep Learning
Book Details
Format
Paperback / Softback
ISBN-10
1617296481
ISBN-13
9781617296482
Publisher
Manning Publications
Imprint
Manning Publications
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Mar 15th, 2024
Print length
450 Pages
Weight
996 grams
Dimensions
18.70 x 23.40 x 3.40 cms
Product Classification:
Software EngineeringMaths for computer scientists
Ksh 11,000.00
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Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. You''ll peer inside the “black box” to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications.
Math and Architectures of Deep Learning sets out the foundations of DL usefully and accessibly to working practitioners. Each chapter explores a new fundamental DL concept or architectural pattern, 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.
Learning 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.
The mathematical paradigms that underlie deep learning 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 Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. Written by deep learning expert Krishnendu Chaudhury, you''ll peer inside the “black box” to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications.
about the technology
It''s important to understand how your deep learning models work, both so that you can maintain them efficiently and explain them to other stakeholders. Learning 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 Learning sets 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 pattern, 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.Get Math and Architectures of Deep Learning by at the best price and quality guaranteed only at Werezi Africa's largest book ecommerce store. The book was published by Manning Publications and it has pages.
