Eli Stevens

Deep Learning with PyTorch, Second Edition

Deep Learning with PyTorch, Second Edition is a hands-on guide to modern machine learning with PyTorch. You’ll discover how easy PyTorch makes it to build your entire DL pipeline, including using the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results. Each new technique you learn is put into action to build a full-size medical image classifier chapter-by-chapter.

In this modernized second edition, you’ll find new coverage of how to develop and train groundbreaking generative AI models. You’ll learn about the foundational building blocks of transformers to create large language models and generate exciting images by building your own diffusion model. Plus, you'll discover ways to improve your results by training with augmented data, make improvements to the model architecture, and perform fine tuning.

Deep Learning with PyTorch

Deep Learning with PyTorch teaches you to create neural networks and deep learning systems with PyTorch. This practical book quickly gets you to work building a real-world example from scratch: a tumor image classifier. Along the way, it covers best practices for the entire DL pipeline, including the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results. After covering the basics, the book will take you on a journey through larger projects. The centerpiece of the book is a neural network designed for cancer detection. You'll discover ways for training networks with limited inputs and start processing data to get some results. You'll sift through the unreliable initial results and focus on how to diagnose and fix the problems in your neural network. Finally, you'll look at ways to improve your results by training with augmented data, make improvements to the model architecture, and perform other fine tuning.