Explaining How Pytorch Back Propagation Works ?

How does backpropagation actually work inside PyTorch? In this video, we break down the core idea behind backpropagation and how automatic differentiation (Autograd) powers deep learning in PyTorch. Instead of treating it like magic, we walk through how gradients are computed, how the computational graph is built, and how errors flow backward through the network. If you're building your own deep learning library (especially in C like this series), understanding this is absolutely critical. We’ll cover: What backpropagation really is How PyTorch builds and uses a computational graph How gradients are calculated step-by-step The role of tensors and .grad How this connects to your own Autograd implementation This video is part of the series where we recreate deep learning components from scratch — so you don’t just use frameworks, you understand them. 🔗 Useful Links: GitHub Repo: https://www.github.com/umairgillani93/miniTorch 📌 Who is this for? Developers building ML from scratch Anyone confused about backpropagation People who want to go beyond “black-box” deep learning 🚀 Next Videos: We’ll implement more components like loss functions, optimizers, and full transformer blocks.