Overview

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

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