You Don't Understand Neural Networks Without This

Backpropagation Explained Step By Step Along With Implementation & Debugging 🚀 In this video, we break down Backpropagation from the ground up — step by step — while also implementing and debugging it live in C. If you’ve ever wondered how neural networks actually learn, this walkthrough will help you understand the gradients, computational graph traversal, chain rule, and parameter updates in the most practical way possible. We’ll go through: ✅ How gradients flow through a computational graph ✅ Chain Rule intuition for deep learning ✅ Backward pass implementation in C ✅ Gradient accumulation & graph traversal ✅ Live debugging of backpropagation issues ✅ How frameworks like PyTorch perform autograd internally This is part of my journey of building a deep learning / autograd framework completely from scratch in C. 💻 ⭐ GitHub Repository: miniTorch GitHub 📌 If you enjoy low-level AI engineering, neural networks, transformers, and systems programming content, make sure to subscribe and follow the journey! #DeepLearning #Backpropagation #Autograd #MachineLearning #NeuralNetworks #CProgramming #LLM #AI #PyTorch #Transformers