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Math and Architectures of Deep Learning: the math behind the algorithms

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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.

Learn more about the book here: https://www.manning.com/books/math-and-architectures-of-deep-learning

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Math and Architectures of Deep Learning: the math behind the algorithms

  1. 1. Understanding the Math behind the Algorithms With Math and Architectures of Deep Learning. Take 42% off by entering slchaudhury into the discount code box at checkout at manning.com.
  2. 2. The mathematical paradigms that underpin deep learning typically start out as dense academic papers, often leaving engineers in the dark about how their models actually function. Learning mathematical foundations and neural network architecture can be challenging, but the payoff is big.
  3. 3. 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. 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.
  4. 4. 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 underlying mathematics and demonstrating how they work in practice with well-annotated Python code.
  5. 5. What people are saying about the book: Gives a unique perspective about machine learning and mathematical approaches. -Krzysztof Kamyczek This is a book that will reward your patience and perseverance with a clear and detailed knowledge of deep learning mathematics and associated techniques. -Tony Holdr
  6. 6. About the author: Krishnendu Chaudhury is a deep learning and computer vision expert with decade-long stints at both Google and Adobe Systems under his belt. He is presently CTO and co- founder of Drishti Technologies. He has a PhD in computer science from the University of Kentucky at Lexington.
  7. 7. Take 42% off Math and Architectures of Deep Learning by entering slchaudhury into the discount code box at checkout at manning.com. If you want to learn more about the book, you can check it on our browser- based liveBook reader here.

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