Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Learn the fundamentals of PyTorch in this comprehensive tutorial lecture from the University of Utah's Data Science program. Explore essential deep learning framework concepts, starting with the distinction between dynamic and static frameworks and PyTorch's historical development. Master GPU utilization, work with numpy examples, and understand PyTorch statistics and abstractions. Practice hands-on with Python tensors, GPU operations, gradient recording, and model implementation. Dive into practical code examples covering device availability checks, autograde functionality, data loaders, batch processing, and essential Python imports. Gain experience with class implementations, linear operations, basic logic structures, and results analysis through real-world programming demonstrations.