Home

Books

Computers & Technology

Basic Computing

Math and Architectures of Deep Learning

303.00

Inclusive of VAT

Free Delivery

Only 5 left in stock

Free Delivery

Get it by 29 May - 3 June

Order in 15 h 28 m

Delivery by noon

High Rated Seller

Cash on Delivery

Secure Transaction

Explore other bestsellers in
Basic Computing

BANK OFFERS

Earn  15.15 cashback with the Mashreq noon Credit Card.

SELLER

Item as shown

Partner since

Free delivery on Lockers & Pickup Points

This item is eligible for free returns

Product Overview

Specifications

Publisher	Pearson Education
ISBN 13	9781617296482
ISBN 10	1617296481
Author	Krishnendu Chaudhury
Book Format	Paperback

Language	English
Book Description	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.
Publication Date	20240315
Number of Pages	450

Cart Total  303.00