Deep Learning with PyTorch Training and applying deep learning and generative AI models, 2nd Edition (Howard Huang, Eli Stevens, Luca Antiga etc.)（Z-Library）

Author： Howard Huang, Eli Stevens, Luca Antiga, Thomas Viehmann

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 this book 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 reader For Python programmers with a background in machine learning.

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