Practical Deep Learning - A Python-Based Introduction

Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects.

If you ve been curious about machine learning but didn t know where to start, this is the book you 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 math the book will cover the rest. After an introduction to Python, you ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models performance.

You ll also learn:
How to use classic machine learning models lik e k-Nearest Neighbors, Random Forests, and Support Vector MachinesHow neural networks work and how they re trainedHow to use convolutional neural networksHow to develop a successful deep learning model from scratch
You ll conduct experiments along the way, building to a final case study that incorporates everything you ve learned.

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.

Inhaltsverzeichnis:

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

Rezension:

"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