02320nam a22002897a 4500 20260727151005.0 260727s2026 |||||||| |||| 00| 0 eng d 9781633438859 PK-LaUMT 005.133 DEE- Deep learning with PyTorch : training and applying deep learning and generative AI models / Luca Antiga . . . [et al.] 2nd ed. Shelter Island : Manning, 2026 xxviii, 514 p. Index present 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 Eng Python (Computer program language) 3584 PyTorch (electronic resource) 14263 Machine learning Deep learning Antiga, Luca 14264 Stevens, Eli 14265 Huang, Howard 14266 Viehmann, Thomas 14267 BK 141676 141676 0 0 0 0 01 01 2026-07-27 0 005.133 DEE- 153669 2026-07-27 15:10:18 2026-07-27 BK