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