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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