Practical Deep Learning
A Python-Based Introduction
Published on 23. February 2021
Book
Paperback/Softback
464 pages
978-1-7185-0074-7 (ISBN)
Description
Deep Learning for Complete Beginners: A Python-Based Introduction is for complete beginners in machine learning. It introduces fundamental concepts such as classes and labels, building a dataset, and what a model is and does before presenting classic machine learning models, neural networks, and modern convolutional neural networks. Experiments in Python - working with leading open-source toolkits and standard datasets - give the reader hands-on experience with each model and help them build intuition about how to transfer the examples in the book to their own projects.
Reviews / Votes
"Practical Deep Learning with Python is the perfect book for someone looking to break into deep learning. This book achieves an ideal balance between explaining prerequisite introductory material and exploring nuanced subtleties of the methods described. The reader will come away with a solid foundational understanding of the content as well as the practical knowledge required to apply the methods to real-world problems. Deep learning will continue to enable many breakthroughs in artificial intelligence applications and this book covers all that is needed to springboard into this exciting field."-Matt Wilder, longtime neural network practitioner and owner of Wilder AI, a deep learning consulting company
"Kneusel's book tackles machine learning (classification) fantastically, helping anyone with an interest to learn and turning that interest into a skillset for future machine learning projects."
-GeekDude, GeekTechStuff
More details
Language
English
Place of publication
San Francisco
United States
Dimensions
Height: 234 mm
Width: 182 mm
Thickness: 32 mm
Weight
774 gr
ISBN-13
978-1-7185-0074-7 (9781718500747)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
Book
07/2025
2nd Edition
No Starch Press
€65.00
Available immediately
Additional editions
E-Book
03/2021
No Starch Press
€46.99
Available for download
Person
Ron Kneusel has been working in the machine learning industry since 2003 and has been programming in Python since 2004. He received a PhD in Computer Science from UC Boulder in 2016 and is the author of two previous books: Numbers and Computers and Random Numbers and Computers.
Content
Foreword by Michael C. Mozer, PhD
Acknowledgments
Introduction
Chapter 1: Getting Started
Chapter 2: Using Python
Chapter 3: Using NumPy
Chapter 4: Working With Data
Chapter 5: Building Datasets
Chapter 6: Classical Machine Learning
Chapter 7: Experiments with Classical Models
Chapter 8: Introduction to Neural Networks
Chapter 9: Training A Neural Network
Chapter 10: Experiments with Neural Networks
Chapter 11: Evaluating Models
Chapter 12: Introduction to Convolutional Neural Networks
Chapter 13: Experiments with Keras and MNIST
Chapter 14: Experiments with CIFAR-10
Chapter 15: A Case Study: Classifying Audio Samples
Chapter 16: Going Further
Index
Acknowledgments
Introduction
Chapter 1: Getting Started
Chapter 2: Using Python
Chapter 3: Using NumPy
Chapter 4: Working With Data
Chapter 5: Building Datasets
Chapter 6: Classical Machine Learning
Chapter 7: Experiments with Classical Models
Chapter 8: Introduction to Neural Networks
Chapter 9: Training A Neural Network
Chapter 10: Experiments with Neural Networks
Chapter 11: Evaluating Models
Chapter 12: Introduction to Convolutional Neural Networks
Chapter 13: Experiments with Keras and MNIST
Chapter 14: Experiments with CIFAR-10
Chapter 15: A Case Study: Classifying Audio Samples
Chapter 16: Going Further
Index