Inside Deep Learning: Math, Algorithms, Models

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.