Backpropagation and Multi-Layer Perceptrons from Scratch

Have you ever wondered what actually happens under the hood when a neural network learns? To truly master deep learning, you must look beyond high-level libraries and grasp the core mechanics of how data flows and gradients update. This text-based course guides you through the fundamental mathematics and structural building blocks of modern neural networks. You will transition from theoretical concepts to writing clean, explicit code that trains a model from the ground up. By reading through clear, step-by-step explanations, you will build a solid intuition for neural network architecture. You will learn to write matrix multiplications, activation functions, and optimization steps without relying on automated magic. What you will learn: Understand the foundational math behind Multi-Layer Perceptrons and matrix operations; Implement backpropagation manually to see how gradients flow through a network; Write custom activation functions and loss calculations using PyTorch tensors; Apply modern initialization techniques to prevent vanishing or exploding gradients; Configure basic training loops and optimization steps from scratch; Read and analyze tensor shapes to debug network architecture issues. The course starts with essential mathematical definitions and basic tensor operations before gradually guiding you through building a Multi-Layer Perceptron, implementing the backward pass, and optimizing parameters. This course is designed for programmer-level beginners who have basic Python knowledge and want to understand the inner workings of deep learning. No prior machine learning experience is required. Begin reading today to demystify the core algorithms powering modern artificial intelligence.