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Deep Learning with PyTorch, Second Edition: Training and applying deep learning and generative AI models
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
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
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. 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 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.
About the “Deep Learning with PyTorch, Second Edition” shows you how to build neural network models using the latest version of PyTorch. Along the way you’ll learn techniques for training using augmented data, improving model architecture, and fine tuning.
About the For Python programmers with a background in machine learning.
About the 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”.
PLEASE When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.
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
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
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. 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 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.
About the “Deep Learning with PyTorch, Second Edition” shows you how to build neural network models using the latest version of PyTorch. Along the way you’ll learn techniques for training using augmented data, improving model architecture, and fine tuning.
About the For Python programmers with a background in machine learning.
About the 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”.
PLEASE When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.
Audible Audio
Published July 2, 2026
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