Why Every AI Model is Just a Tensor Operation?

Tired of staring at RuntimeError: shape mismatch for hours? You've learned scalars, vectors, and matrix multiplication—but none of them move through a neural network without being sliced, diced, reshaped, and broadcasted. Deep Learning doesn't run on "magic"; it runs on the logistics of Tensor Operations. In this episode of Neural Nexus, we decode the "Operating System" of deep learning. We move past the definitions to show you why reshape vs view matters, how broadcasting saves your memory, and why permute is the single most important command for preparing data for Transformer attention. In this deep dive, you will learn: The Hierarchy: Why we need 0D, 1D, 2D, 3D, and 4D tensors to represent the world. The "Rubik's Cube" Intuition: How to visualize reshape, transpose, and reduction. Broadcasting: The most powerful (and most dangerous) operation in PyTorch. The Contiguity Secret: Why view fails on transposed tensors and how strides control your GPU memory. Pro-Tips: Why keepdim=True will save your training loop from silent broadcasting bugs. If you want to master the "logistics" of AI architecture and write code that actually runs the first time, this is the lesson you need.