DeepSite - 🧬 RemixDevelop intuition for tensor operations through interactive exercises
Tensors are multi-dimensional arrays that generalize scalars (0D), vectors (1D), and matrices (2D) to higher dimensions. In PyTorch, tensors are the fundamental data structure used for all computations.
Key Insight: Tensors are defined by their shape (dimensions) and contain elements of a single data type (e.g., float32).
Mastering tensor operations is essential for efficient deep learning implementations. This playground helps you build intuition through visualization and interactive exercises.
Your tensor will appear here
Shape: None
Size: 0 elements
Change tensor dimensions while maintaining total size
Swap two dimensions of a tensor
How tensors of different shapes are combined
Shape: (2, 3)
Your reshaped tensor will appear here
Elements maintain their order but are reorganized to fit the new shape.
Help others learn by contributing your own tensor exercises, quiz questions, or visual examples. The best submissions will be added to the playground!
Note: All contributions should focus on building intuition about tensor operations and their applications in PyTorch.