## Book description

If youÃ?Â¢??ve been curious about machine learning but didnÃ?Â¢??t know where to start, this is the book youÃ?Â¢??ve been waiting for. Focusing on the subfield of machine learning known as *deep learning*, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, *Practical Deep Learning* teaches you the why of deep learning and will inspire you to explore further.

All you need is basic familiarity with computer programming and high school mathÃ?Â¢??the book will cover the rest. After an introduction to Python, youÃ?Â¢??ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your modelsÃ?Â¢?? performance.

YouÃ?Â¢??ll also learn:

Ã?Â¢?Ã?Â¢How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines

Ã?Â¢?Ã?Â¢How neural networks work and how theyÃ?Â¢??re trained

Ã?Â¢?Ã?Â¢How to use convolutional neural networks

Ã?Â¢?Ã?Â¢How to develop a successful deep learning model from scratch

YouÃ?Â¢??ll conduct experiments along the way, building to a final case study that incorporates everything youÃ?Â¢??ve learned. All of the code youÃ?Â¢??ll use is available at the linked examples repo.

The perfect introduction to this dynamic, ever-expanding field, *Practical Deep Learning* will give you the skills and confidence to dive into your own machine learning projects.

## Publisher resources

## Table of contents

- Cover Page
- Title Page
- Copyright Page
- Dedication
- About the Author
- About the Technical Reviewer
- BRIEF CONTENTS
- CONTENTS IN DETAIL
- FOREWORD
- ACKNOWLEDGMENTS
- INTRODUCTION
- 1 GETTING STARTED
- 2 USING PYTHON
- 3 USING NUMPY
- 4 WORKING WITH DATA
- 5 BUILDING DATASETS
- 6 CLASSICAL MACHINE LEARNING
- 7 EXPERIMENTS WITH CLASSICAL MODELS
- 8 INTRODUCTION TO NEURAL NETWORKS
- 9 TRAINING A NEURAL NETWORK
- 10 EXPERIMENTS WITH NEURAL NETWORKS
- 11 EVALUATING MODELS
- 12 INTRODUCTION TO CONVOLUTIONAL NEURAL NETWORKS
- 13 EXPERIMENTS WITH KERAS AND MNIST
- 14 EXPERIMENTS WITH CIFAR-10
- 15 A CASE STUDY: CLASSIFYING AUDIO SAMPLES
- 16 GOING FURTHER
- INDEX

## Product information

- Title: Practical Deep Learning
- Author(s):
- Release date: March 2021
- Publisher(s): No Starch Press
- ISBN: 9781718500747

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