Ronald T. Kneusel

Practical Deep Learning, 2nd Edition (eBook, ePUB)

• Format: ePub

Produktbeschreibung

Practical Deep Learning, 2nd Edition is your gateway into AI, equipping you with the knowledge and confidence to build powerful AI models using the latest architectures and techniques. If you've been curious about artificial intelligence and 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, 2nd Edition 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 like k-Nearest Neighbors, Random Forests, and Support Vector Machines
• How neural networks work and how they're trained
• How to use convolutional neural networks
• How 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. This second edition is thoroughly revised and updated, and adds six new chapters to further your exploration of deep learning from basic CNNs to more advanced models. New chapters cover fine tuning, transfer learning, object detection, semantic segmentation, multilabel classification, self-supervised learning, generative adversarial networks, and large language models. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning, 2nd Edition will give you the skills and confidence to dive into your own machine learning projects.

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Produktdetails

• Verlag: No Starch Press
• Seitenzahl: 624
• Erscheinungstermin: 8. Juli 2025
• Englisch
• ISBN-13: 9781718504219
• Artikelnr.: 72259651

Autorenporträt

Ron works in industry building deep learning (AI) systems. He also has extensive experience with medical imaging and developing medical devices. Ron has been working in machine learning since 2003, and deep learning since before AlexNet was a thing. Ron's Ph.D. in computer science is from the University of Colorado, Boulder (2016). Ron also holds a master's degree in physics from Michigan State University and an undergraduate degree in physics and mathematics from Valparaiso University. Exactly when those particular degrees were awarded has been lost to the ages, but it was some time in the latter decades of the 20th century. Ron's interests vary greatly but always orbit science, math, and technology. A child of the personal computer revolution in the late 1970s and early 1980s, Ron has been coding for longer than he's been able to drive. And, remarkably, it remains as fresh an enterprise now as it was all those decades ago.

Inhaltsangabe

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