Mastering PyTorch Basics: A Detailed Explanation of Tensor Operations and Automatic Differentiation

This article introduces the basics of Tensors in PyTorch. Tensors are the fundamental units for storing and manipulating data, similar to NumPy arrays but with GPU acceleration support, making them a core structure of neural networks. Creation methods include converting from lists/NumPy arrays (`torch.tensor()`/`as_tensor()`) and using constructors like `zeros()`/`ones()`/`rand()`. Key attributes include shape (`.shape`/`.size()`), data type (`.dtype`), and device (`.device`), which can be converted via `.to()`. Major operations cover arithmetic (addition, subtraction, multiplication, division, matrix multiplication), indexing/slicing, reshaping (`reshape()`/`squeeze()`/`unsqueeze()`), and concatenation/splitting (`cat()`/`stack()`/`split()`). Autograd is central: `requires_grad=True` enables gradient tracking, `backward()` computes gradients, and `grad` retrieves them. Important considerations include handling gradients of non-leaf nodes, gradient accumulation, and `detach()` for tensor separation. Mastering tensor operations and autograd is foundational for neural network learning.