Chaudhury Krishnendu
Chaudhury Krishnendu - Math And Architectures Of Deep Learning - Paperback
Chaudhury Krishnendu - Math And Architectures Of Deep Learning - Paperback
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Description: 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 Py Torch. 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.
Title: Math And Architectures Of Deep Learning
Author(s): Chaudhury Krishnendu
Publisher: Manning Publications
Barcode: 9781617296482
Pages: 450 Pages
Publication Date: 3/26/2024
Category: Maths For Computer Scientists
Description: 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 Py Torch. 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.
Title: Math And Architectures Of Deep Learning
Author(s): Chaudhury Krishnendu
Publisher: Manning Publications
Barcode: 9781617296482
Pages: 450 Pages
Publication Date: 3/26/2024
Category: Maths For Computer Scientists
