Practical Deep Learning, 2nd Edition

A Python-Based Introduction

by Ronald T. Kneusel

July 2025, 584 pp

ISBN-13:

9781718504202

Download Chapter 12: A Case Study: Classifying Audio Samples

Dip into deep learning without drowning in theory with this fully updated edition of Practical Deep Learning from experienced author and AI expert Ronald T. Kneusel.

After a brief review of basic math and coding principles, you’ll dive into hands-on experiments and learn to build working models for everything from image analysis to creative writing, and gain a thorough understanding of how each technique works under the hood. Whether you’re a developer looking to add AI to your toolkit or a student seeking practical machine learning skills, this book will teach you:

How neural networks work and how they’re trained
How to use classical machine learning models
How to develop a deep learning model from scratch
How to evaluate models with industry-standard metrics
How to create your own generative AI models

Each chapter emphasizes practical skill development and experimentation, building to a case study that incorporates everything you’ve learned to classify audio recordings. Examples of working code you can easily run and modify are provided, and all code is freely available on GitHub. With Practical Deep Learning, second edition, you’ll gain the skills and confidence you need to build real AI systems that solve real problems.

New to this edition: Material on computer vision, fine-tuning and transfer learning, localization, self-supervised learning, generative AI for novel image creation, and large language models for in-context learning, semantic search, and retrieval-augmented generation (RAG).

Author Bio

Ronald T. Kneusel earned a PhD in machine learning from the University of Colorado, Boulder, and has over 20 years of machine learning experience in industry. Kneusel is also the author of numerous books, including Math for Programming (2025), The Art of Randomness (2024), How AI Works (2023), Strange Code (2022), and Math for Deep Learning (2021), all from No Starch Press.

Table of contents

Foreword
Introduction
Chapter 0: Environment and Mathematical Preliminaries
Part I: Data Is Everything
Chapter 1: It’s All About the Data
Chapter 2: Building the Datasets
Part II: Classical Machine Learning
Chapter 3: Introduction to Machine Learning
Chapter 4: Experiments with Classical Models
Part III: Neural Networks
Chapter 5: Introduction to Neural Networks
Chapter 6: Training a Neural Network
Chapter 7: Experiments with Neural Networks
Chapter 8: Evaluating Models
Part IV: Convolutional Neural Networks
Chapter 9: Introduction to Convolutional Neural Networks
Chapter 10: Experiments with Keras and MNIST
Chapter 11: Experiments with CIFAR-10
Chapter 12: A Case Study: Classifying Audio Samples
Part V: Advanced Networks and Generative AI
Chapter 13: Advanced CNN Architectures
Chapter 14: Fine-Tuning and Transfer Learning
Chapter 15: From Classification to Localization
Chapter 16: Self-Supervised Learning
Chapter 17: Generative Adversarial Networks
Chapter 18: Large Language Models
Afterword

The chapters in red are included in this Early Access PDF.