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Learn the fundamentals of PyTorch through this comprehensive applied tutorial that covers essential concepts from basic tensors to building complete machine learning models. Master PyTorch tensors and the automatic differentiation system (Autograd) before diving into data handling with the Dataset class for both NLP and computer vision applications. Explore how to efficiently load and batch data using DataLoader, then progress to implementing linear regression models with proper training and validation loops. Gain practical understanding of the torch.nn module for building neural networks, with hands-on examples that demonstrate real-world applications across different domains including natural language processing and image processing tasks.
