training and applying deep learning and generative AI models Antiga, Luca Stevens, Eli Huang, Howard Viehmann, Thomas text Shelter Island Manning 2026 2nd ed. monographic eng

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xxviii, 514 p. 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 you⁰́₉ll 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 you⁰́₉ll learn how to create your own neural network and deep learning systems and take full advantage of PyTorch⁰́₉s built-in tools for automatic differentiation, hardware acceleration, distributed training, and more. You⁰́₉ll 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 Luca Antiga . . . [et al.] Index present Eng Python (Computer program language) PyTorch (electronic resource) Machine learning Deep learning 005.133 DEE- 9781633438859 260727 20260727151005.0