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13 pages in this section.
Why tensors, autograd, the training loop, and GPU/mixed-precision training are one machine rather than four separate topics - the mental model behind every other page in this section.
Learn PyTorch basics with 10 examples. Understand tensors, their operations, and how to leverage GPU acceleration for deep learning.
Learn how PyTorch Autograd automatically computes gradients for tensors, enabling neural network training and debugging custom layers and loss functions.
Learn to build PyTorch models by subclassing nn.Module. Define layers, implement forward passes, and manage parameters for training.
Learn to use PyTorch Dataset and DataLoader for efficient data pipelines. Implement custom datasets, apply transforms, and optimize data loading for deep learning.
Learn to implement PyTorch training loops, including forward and backward passes, loss computation, and optimizer steps. Manage learning rates and save model progress.
Learn best practices for reproducible, efficient PyTorch deep learning. Optimize setup, data pipelines, training, and deployment for multi-GPU scaling.
A single-page roundup of every highlight bullet from the 12 pages in the Deep Learning section, grouped by source page so you can scan all 49 takeaways without opening each article individually.