Learning PyTorch: A Roadmap

Begin with tensors: creation, indexing, shape changes, broadcasting, device placement, and basic arithmetic. Learn how tensor operations construct a computation graph and how autograd calculates gradients. Check gradients on a simple scalar example before introducing a large model. Understand when gradients accumulate and why training loops clear them between updates. Next, learn the module system. Define layers and parameters in an nn.Module, choose a loss function, and use an optimizer to update parameters. Separate training and evaluation behavior: modules such as dropout or batch normalization behave differently under training and inference modes. Build a Dataset and DataLoader for batching, shuffling, and data access. Keep preprocessing consistent with the model's expected inputs. Implement a transparent training loop before adopting higher-level abstractions. Track loss and task metrics on validation data, save checkpoints, and stop or tune based on a clear protocol. Keep a final test set unused during selection. Learn how to save and load model state safely, reconstruct the architecture, and move tensors and modules to the correct device. After fundamentals, explore transfer learning, convolutional networks, sequence models, or transformers based on your target task. Learn mixed precision, distributed training, compilation, and profiling when a real workload requires them. These features can add complexity; first establish a correct and measurable baseline. A strong project includes data provenance, split design, preprocessing, training configuration, evaluation, and inference code. Add tests for small data transformations and one end-to-end smoke path. Document limitations and compare with a simple baseline. PyTorch expertise is demonstrated by diagnosing behavior and building a reliable workflow, not by using every available feature.