Math and Architectures of Deep Learning - by Krishnendu Chaudhury (Paperback)

About the Book

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. 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.
Math and Architectures of Deep Learning sets out the foundations of DL usefully and accessibly 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.
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.

Book Synopsis

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.

Foreword by Prith Banerjee.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

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.

Table of Contents

1 An overview of machine learning and deep learning
2 Vectors, matrices, and tensors in machine learning
3 Classifiers and vector calculus
4 Linear algebraic tools in machine learning
5 Probability distributions in machine learning
6 Bayesian tools for machine learning
7 Function approximation: How neural networks model the world
8 Training neural networks: Forward propagation and backpropagation
9 Loss, optimization, and regularization
10 Convolutions in neural networks
11 Neural networks for image classification and object detection
12 Manifolds, homeomorphism, and neural networks
13 Fully Bayes model parameter estimation
14 Latent space and generative modeling, autoencoders, and variational autoencoders
A Appendix

From the Back Cover

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.
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.

Review Quotes

'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 Holdroyd 'Most online machine learning courses teach you how to get stuff done, but they don't give you the underlying math. If you want to know, this is the book for you!' Wiebe de Jong 'A really interesting book for people that want to understand the underlying mathematical mechanism of deep learning.' Julien Pohie 'Gives a unique perspective about machine learning and mathematical approaches.' Krzysztof Kamyczek 'An awesome book to get the grasp of the important mathematical skills to understand the very basics of deep learning.' Nicole Koenigstein

About the Author

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.