Deep Learning with PyTorch, Second Edition, Video Edition

Everything you need to create neural networks with PyTorch, including Large Language and diffusion models.

PyTorch core developer Howard Huang updates the bestselling original Deep Learning with PyTorch with new insights into the transformers architecture and generative AI models.

In Deep Learning with PyTorch, Second Edition you’ll find
Deep learning fundamentals reinforced with hands-on projects
Mastering PyTorch’s flexible APIs for neural network development
Implementing CNNs, transformers, and diffusion models
Optimizing models for training and deployment
Generative AI models to create images and text
Instantly familiar to anyone who knows PyData tools like NumPy, PyTorch simplifies deep learning without sacrificing advanced features. In Deep Learning with PyTorch, Second Edition you’ll learn how to create your own neural network and deep learning systems and take full advantage of PyTorch’s built-in tools for automatic differentiation, hardware acceleration, distributed training, and more. 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 with practical code examples in each chapter, culminating into you building your own convolution neural networks, transformers, and even a real-world medical image classifier.

About the Technology
The powerful PyTorch library makes deep learning simple—without sacrificing the features you need to create efficient neural networks, LLMs, and other ML models. Pythonic by design, it’s instantly familiar to users of NumPy, Scikit-learn, and other ML frameworks. This thoroughly-revised second edition covers the latest PyTorch innovations, including how to create and refine generative AI models.

About the Book
Deep Learning with PyTorch, Second Edition shows you how to build neural network models using the latest version of PyTorch. Clear explanations and practical projects help you master the fundamentals and explore advanced architectures including transformers and LLMs. Along the way you’ll learn techniques for training using augmented data, improving model architecture, and fine tuning.

What’s Inside
PyTorch APIs for neural network development
LLMs, transformers, and diffusion models
Model training and deployment

About the Reader
For Python programmers with a background in machine learning.

About the Authors
Howard Huang is a software engineer and developer on the PyTorch library focusing on large scale, distributed training. Eli Stevens, Luca Antiga, and Thomas Viehmann authored the first edition of Deep Learning with PyTorch.