Master PyTorch 2.10.0 | 04A
🚀 Course: Master PyTorch 2.10.0
🔁 Module 04: Data and Dataloaders
🧠 Lecture A: Datasets and Loaders
👇 Link to the notebook:
https://tinyurl.com/239kg7vd
🚀 PyTorch Datasets and DataLoaders tutorial: learn how to build custom Dataset classes, choose between map-style vs iterable datasets, and configure efficient DataLoader pipelines for batching, shuffling, multiprocessing workers, and smart batching with padding. This lecture walks through core dataset methods, on-the-fly transforms, memory pinning, and tuning DataLoader workers using practical PyTorch, torchvision, and torchtext examples, plus TensorFlow demos for comparison. You will also see how to use built-in image and text datasets, manage dataset downloads and caching, and design scalable data pipelines for large, streaming, or variable-length data. Watch to upgrade your deep learning data loading skills.
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