PyTorch Autograd: Foundations of Automatic Differentiation

Deep learning models rely on gradient descent to learn, but calculating complex derivatives manually is incredibly difficult. PyTorch Autograd automates this entire process, serving as the computational engine behind modern neural networks. In this text-only course, you will demystify automatic differentiation from the ground up. You will explore how PyTorch builds dynamic computational graphs under the hood and learn to control gradient tracking to write more efficient, bug-free training loops. What you'll learn: Understand the core concepts of computational graphs and backpropagation; Configure PyTorch tensors to track operations using gradient properties; Compute gradients of complex functions using the backward method; Prevent gradient accumulation issues by resetting gradients correctly; Apply modern memory-saving techniques like inference mode for model evaluation; Debug common gradient errors in custom training loops. We begin by establishing key mathematical terms and foundational tensor operations. From there, you will read through step-by-step code explanations that demonstrate how to manage gradients during both training and evaluation phases. This course is designed for beginner-to-intermediate Python developers and aspiring machine learning engineers who want to understand the mechanics of deep learning frameworks. No prior experience with advanced calculus or PyTorch is required. Start reading today to unlock the full potential of automatic differentiation in your machine learning projects.