Step-by-Step Gradient Descent for Regression in PyTorch

Gradient descent is the engine behind modern machine learning, yet its inner workings can feel like a black box. Understanding each step of this optimization process is essential for building, debugging, and scaling neural networks successfully. In this text-based course, you will demystify gradient descent by breaking it down into distinct, readable phases. You will learn how to initialize parameters, compute loss, perform backpropagation, and update weights using PyTorch's core tensor operations and optimization modules. By reading through clear explanations and structured code snippets, you will gain a deep conceptual and practical understanding of how models actually learn. What you'll learn: - Understand the mathematical intuition behind gradient descent and loss functions - Create synthetic datasets and structure them using PyTorch tensors - Implement the five core steps of the gradient descent loop from scratch - Apply PyTorch autograd to automatically compute gradients without manual calculus - Configure optimization algorithms using modern PyTorch design patterns - Write clean, readable training loops utilizing Python type hints for better code safety You will start with the fundamental terminology of optimization and linear regression before moving on to practical implementation. Through detailed written explanations and structured code walk-throughs, you will build and train a linear regression model step by step. This course is designed for beginners in machine learning and Python developers who want a solid, conceptual understanding of training loops. No prior experience with PyTorch or advanced calculus is required. Start reading today to build a rock-solid foundation in machine learning optimization.