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 doesn’t have to be a black box! Knowing how your models and algorithms actually work gives you greater control over your results. And you don’t have to be a mathematics expert or a senior data scientist to grasp what’s going on inside a deep learning system. This book gives you the practical insight you need to understand and explain your work with confidence.

About the book

Inside Deep Learning

illuminates the inner workings of deep learning algorithms in a way that even machine learning novices can understand. You’ll explore deep learning concepts and tools through plain language explanations, annotated code, and dozens of instantly useful PyTorch examples. Each type of neural network is clearly presented without complex math, and every solution in this book can run using readily available GPU hardware!

What's inside

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

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.

Table of Contents

PART 1 FOUNDATIONAL METHODS

1 The mechanics of learning

2 Fully connected networks

3 Convolutional neural networks

4 Recurrent neural networks

5 Modern training techniques

6 Common design building blocks

PART 2 BUILDING ADVANCED NETWORKS

7 Autoencoding and self-supervision

8 Object detection

9 Generative adversarial networks

10 Attention mechanisms

11 Sequence-to-sequence

12 Network design alternatives to RNNs

13 Transfer learning

14 Advanced building blocks

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