Building a XOR Multilayer Perceptron from Scratch with NumPy

Deep learning models can seem like black boxes until you build them yourself from the ground up. Understanding how a neural network processes data and updates its weights is the key to mastering modern artificial intelligence. This course guides you through the fundamental mathematics and mechanics of neural networks by solving the classic XOR classification problem, which cannot be solved by a single-layer network. You will transition from understanding basic linear separability to writing clean, vectorized Python code. What you'll learn: - Understand the foundational theory of neural networks, activation functions, and why the XOR problem requires a hidden layer - Implement forward propagation to pass data through a multilayer network using NumPy - Code the backpropagation algorithm from scratch using gradient descent and chain-rule calculus - Configure batch training loops to update network weights and minimize loss - Apply modern Python development practices, including clean code structure and basic type hints, to your machine learning scripts This text-based course starts with essential mathematical definitions and conceptual breakdowns before moving into step-by-step code implementation. You will explore how weights, biases, and learning rates interact to train a functioning model. Designed for beginners, this course requires only basic Python knowledge and no prior machine learning experience. Start your deep learning journey by mastering the core mechanics of neural networks today.