Stochastic Gradient Descent and Perceptrons with NumPy

Understanding how neural networks learn is the foundation of modern artificial intelligence. This text-based course guides you through the mechanics of Stochastic Gradient Descent (SGD), the fundamental optimization algorithm used to train machine learning models. You will transition from theoretical math formulas to clean, working Python code. By reading detailed explanations and studying structured code snippets, you will understand how weights and biases update to minimize errors in a simple perceptron model. What you'll learn: Understand the foundational mathematics of gradient descent, weights, and biases; Implement forward propagation to make predictions with a simple perceptron; Calculate loss and error using vectorized NumPy operations; Apply Stochastic Gradient Descent updates to iteratively train your model; Write clean, modern Python code using type hints to structure your machine learning functions. The course begins with core definitions and the mathematical intuition behind optimization before moving into hands-on implementation. You will explore step-by-step how data flows through a perceptron and how feedback loops adjust the model's parameters. This program is designed for beginners with a basic grasp of Python who want to understand the inner workings of machine learning without relying on complex external frameworks. No prior machine learning experience is required. Start reading today to unlock the core mechanics of neural network training.