PyTorch crash course: tensors, autograd, and your first training loop

Updated for PyTorch 2.13, this crash course explains core deep-learning workflows while identifying older PyTorch 1.x patterns that are deprecated or removed, including legacy image transforms, DataLoader iteration syntax, Variable usage, device selection, and unsafe checkpoint-loading practices. It introduces tensors, shapes, slicing, reshaping, NumPy memory-sharing behavior, accelerator placement, and autograd’s computational graphs and accumulating gradients, then develops the standard training sequence of forward pass, loss calculation, backpropagation, gradient clearing, and parameter updates. Examples progress from manually implemented linear regression to an nn.Module-based MNIST classifier and a CIFAR-10 convolutional neural network, covering datasets, DataLoaders, modern torchvision v2 transforms, logits and CrossEntropyLoss, evaluation mode, inference without gradient tracking, shape debugging, and saving and restoring state dictionaries with weights-only loading. The course also presents torch.compile as a potential performance optimization and argues that training custom speech models is most appropriate when modeling itself is a product advantage, proprietary data or offline deployment is essential, or no suitable service exists, whereas hosted transcription APIs may be more practical for teams primarily building products that use speech recognition.

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