Math and Architectures of Deep Learning

Krishnendu Chaudhury

ISBN 10: 1617296481 / ISBN 13: 9781617296482

Verlag: Manning Mär 2024, 2024

Beschreibung

Neuware -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. YouGÇÖll peer inside the GÇ£black boxGÇ¥ to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications.Manning, St.-Martin-Straße 82, 81541 München 552 pp. Englisch.

Bestandsnummer des Verkäufers 9781617296482

Bibliografische Details

• Titel: Math and Architectures of Deep Learning
• Verlag: Manning Mär 2024
• Erscheinungsjahr: 2024
• Sprache: Englisch
• ISBN-10: 1617296481
• ISBN-13: 9781617296482
• Einband: Taschenbuch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

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.

Über die Autorin bzw. den Autor

Krishnendu Chaudhury  is a deep learning and computer vision expert with decade-long stints at both Google and Adobe Systems. He is presently CTO and co-founder of Drishti Technologies. He has a PhD in computer science from the University of Kentucky at Lexington.