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Math Architectures Deep Learning by Chaudhury Krishnendu (28 results)
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Math and Architectures of Deep Learning
Seller: St Vincent de Paul of Lane County, Eugene, OR, U.S.A.
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Condition: Good. BOOK HAS A BIT OF WAVINESS BUT IS OTHERWISE A GOOD READING COPY. paperback 100% of proceeds go to charity! Good condition with all pages in tact. Item shows signs of use and may have cosmetic defects.
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Math and Architectures of Deep Learning
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Math and Architectures of Deep Learning
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Math and Architectures of Deep Learning
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Math and Architectures of Deep Learning
Published by Manning Publications, 2024
ISBN 10: 1617296481 ISBN 13: 9781617296482
Language: English
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
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US$ 67.66
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Add to basketPaperback. Condition: Brand New. 450 pages. 9.25x7.37x1.12 inches. In Stock.
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Math and Architectures of Deep Learning
Seller: Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, Germany
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Add to basketTaschenbuch. Condition: Neu. 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. 552 pp. Englisch.
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Math and Architectures of Deep Learning
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
US$ 55.98
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Add to basketTaschenbuch. Condition: Neu. 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. 552 pp. Englisch.
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Math and Architectures of Deep Learning (Paperback)
Published by Manning Publications, New York, 2024
ISBN 10: 1617296481 ISBN 13: 9781617296482
Language: English
Paperback. Condition: new. Paperback. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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US$ 55.98
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Add to basketTaschenbuch. Condition: Neu. 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.
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Math and Architectures of Deep Learning
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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US$ 76.43
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US$ 59.28
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Add to basketTaschenbuch. Condition: Neu. 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.
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Math and Architectures of Deep Learning
Published by Manning Publications, 2023
ISBN 10: 1617296481 ISBN 13: 9781617296482
Language: English
US$ 47.22
Convert currencyUS$ 55.47 shipping from Germany to U.S.A.Quantity: 5 available
Add to basketCondition: New. Über den AutorKrishnendu 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 s.
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US$ 95.50
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US$ 108.80
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Add to basketPaperback. Condition: Brand New. 450 pages. 9.25x7.37x1.12 inches. In Stock.
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Math and Architectures of Deep Learning (Paperback)
Published by Manning Publications, New York, 2024
ISBN 10: 1617296481 ISBN 13: 9781617296482
Language: English
US$ 100.15
Convert currencyUS$ 49.29 shipping from United Kingdom to U.S.A.Quantity: 1 available
Add to basketPaperback. Condition: new. Paperback. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Math and Architectures of Deep Learning (Paperback)
Published by Manning Publications, New York, 2024
ISBN 10: 1617296481 ISBN 13: 9781617296482
Language: English
US$ 145.44
Convert currencyUS$ 37.00 shipping from Australia to U.S.A.Quantity: 1 available
Add to basketPaperback. Condition: new. Paperback. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
