This book is ideal for developers and students who want to build deep learning models with PyTorch, combining flexibility, control, and real-world performance.

You will learn to apply modern techniques to design, train, and scale robust neural networks in real-world environments, covering everything from fundamentals to advanced architectures.

Includes:

• Tensor manipulation and Autograd usage

• Modular neural network construction with torch.nn

• Training with DataLoader, optimizers, and loss functions

• Practical application with CNNs, RNNs, Transformers, and GANs

• Integration with PyTorch Lightning, TorchScript, and model export

• Real-world projects with NLP, computer vision, IoT, and cloud deployment

By the end, you will master PyTorch as a professional tool to design scalable AI solutions with technical precision and development agility.

ytorch, deep learning, neural networks, torch.nn, autograd, distributed training, cnn, rnn, transformers, deployment

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

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