Deep learning made simple. Deep learning made simple. 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).

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