Visualizing Gradient Descent with PyTorch and NumPy

Understanding how machine learning models actually learn can feel like looking into a black box. By visualizing gradient descent step-by-step, you demystify the optimization process and gain intuitive control over your model's training behavior. This text-based course guides you through tracking parameter updates, understanding loss landscapes, and selecting the perfect learning rate. You will start with the fundamental mathematics of optimization, defining key terms and exploring the core concepts of loss functions before writing any code. Next, you will implement these concepts from scratch using NumPy, and then transition to PyTorch to build modern, scalable workflows. What you'll learn: Understand the mathematical foundations of gradients, loss functions, and optimization; Practice implementing linear regression from scratch to observe parameter updates; Visualize how different learning rates cause convergence, oscillation, or divergence; Apply PyTorch autograd to automate gradient calculations efficiently; Configure modern optimization algorithms and analyze their training paths. You will begin with foundational definitions, progress through hands-on NumPy implementations, and finish by writing clean, idiomatic PyTorch code. This course is designed for beginners in machine learning and data science who want a deep, conceptual understanding of optimization without needing advanced prerequisites. Start reading today to truly understand how models learn.