Math and Architectures of Deep Learning you own this product

Krishnendu Chaudhury
with Ananya H. Ashok, Sujay Narumanchi, Devashish Shankar
Foreword by Prith Banerjee
  • April 2024
  • ISBN 9781617296482
  • 552 pages
  • printed in black & white

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ePub + Kindle available Apr 16, 2024
Look inside
Shine a spotlight into the deep learning “black box”. This comprehensive and detailed guide reveals the mathematical and architectural concepts behind deep learning models, so you can customize, maintain, and explain them more effectively.

Inside Math and Architectures of Deep Learning you will find:

  • Math, theory, and programming principles side by side
  • Linear algebra, vector calculus and multivariate statistics for deep learning
  • The structure of neural networks
  • Implementing deep learning architectures with Python and PyTorch
  • Troubleshooting underperforming models
  • Working code samples in downloadable Jupyter notebooks

The mathematical paradigms behind deep learning models typically begin as hard-to-read academic papers that leave engineers in the dark about how those 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

Discover what’s going on inside the black box! To work with deep learning you’ll have to choose the right model, train it, preprocess your data, evaluate performance and accuracy, and deal with uncertainty and variability in the outputs of a deployed solution. This book takes you systematically through the core mathematical concepts you’ll need as a working data scientist: vector calculus, linear algebra, and Bayesian inference, all from a deep learning perspective.

about the book

Math and Architectures of Deep Learning teaches the math, theory, and programming principles of deep learning models laid out side by side, and then puts them into practice with well-annotated Python code. You’ll progress from algebra, calculus, and statistics all the way to state-of-the-art DL architectures taken from the latest research.

what's inside

  • The core design principles of neural networks
  • Implementing deep learning with Python and PyTorch
  • Regularizing and optimizing underperforming models

about the reader

Readers need to know Python and the basics of algebra and calculus.

about the author

Krishnendu Chaudhury is co-founder and CTO of the AI startup Drishti Technologies. He previously spent a decade each at Google and Adobe.

Machine learning uses a cocktail of linear algebra, vector calculus, statistical analysis, and topology to represent, visualize, and manipulate points in high dimensional spaces. This book builds that foundation in an intuitive way–along with the PyTorch code you need to be a successful deep learning practitioner.

Vineet Gupta, Google Research

A thorough explanation of the mathematics behind deep learning!

Grigory Sapunov, Intento

Deep learning in its full glory, with all its mathematical details. This is the book!

Atul Saurav, Genworth Financial

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monthly
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$49.99
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only $41.67 per month
  • five seats for your team
  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
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  • choose twelve free products per year
  • exclusive 50% discount on all purchases
  • Math and Architectures of Deep Learning ebook for free