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Math and Architectures of Deep Learning
by Krishnendu Chaudhury
with Ananya H. Ashok, Sujay Narumanchi, Devashish Shankar
Foreword by Prith Banerjee
April 2024
ISBN 9781617296482
552 pages printed in black & white
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.
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.
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