Inside Deep Learning: Math, Algorithms, Models by Edward Raff

Inside Deep Learning: Math, Algorithms, Models by Edward Raff

"If you want to learn some of the deeper explanations of deep learning and PyTorch then read this book!" - Tiklu Ganguly Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems. In Inside Deep Learning, you will learn how to: Implement deep learning with PyTorch Select the right deep learning components Train and evaluate a deep learning model Fine tune deep learning models to maximize performance Understand deep learning terminology Adapt existing PyTorch code to solve new problems Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped—you'll dive into math, theory, and practical applications. Everything is clearly explained in plain English. about the technology Deep learning isn't just for big tech companies and academics. Anyone who needs to find meaningful insights and patterns in their data can benefit from these practical techniques! The unique ability for your systems to learn by example makes deep learning widely applicable across industries and use-cases, from filtering out spam to driving cars. about the book Inside Deep Learning is a fast-paced beginners' guide to solving common technical problems with deep learning. Written for everyday developers, there are no complex mathematical proofs or unnecessary academic theory. You'll learn how deep learning works through plain language, annotated code and equations as you work through dozens of instantly useful PyTorch examples. As you go, you'll build a French-English translator that works on the same principles as professional machine translation and discover cutting-edge techniques just emerging from the latest research. Best of all, every deep learning solution in this book can run in less than fifteen minutes using free GPU hardware! about the reader For Python programmers with basic machine learning skills. about the author Edward Raff is a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library. His research includes deep learning, malware detection, reproducibility in ML, fairness/bias, and high performance computing. He is also a visiting professor at the University of Maryland, Baltimore County and teaches deep learning in the Data Science department. Dr Raff has over 40 peer reviewed publications, three best paper awards, and has presented at numerous major conferences.

“Afantastic book with a colourful and intuitive way of describing how deep learning works” Richard Vaughan

“Amazing at what it does. It's a book for people who not only want to use deep learning, but also understand it!” Adam Slysz

“A remarkably clear explanation of practical deep learning showing readers how to quickly and systematically apply deep learning techniques tosolve their everyday data problems.” Jeff Neumann

“If you want to learn some of the deeper explanations of deep learning and PyTorch then read this book!” Tiklu Ganguly

“A must read if you don't understand how Deep Learning works under the hood.” Abdul Basit Hafeez

Edward Raff is a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library. His research includes deep learning, malware detection, reproducibility in ML, fairness/bias, and high performance computing. He is also a visiting professor at the University of Maryland, Baltimore County and teaches deep learning in the Data Science department. Dr Raff has over 40 peer reviewed publications, three best paper awards, and has presented at numerous major conferences.

SKU	Unavailable
ISBN 13	9781617298639
ISBN 10	1617298638
Title	Inside Deep Learning: Math, Algorithms, Models
Author	Edward Raff
Condition	Unavailable
Binding Type	Paperback
Publisher	Manning Publications
Year published	2022-06-27
Number of pages	425
Cover note	Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
Note	Unavailable