Calculus for Neural Networks: Mastering the Chain Rule

To truly understand how neural networks learn, you must grasp the mathematical engine that drives them: the chain rule. This text-only course demystifies the calculus behind deep learning, turning complex formulas into clear, intuitive concepts. You will transition from basic algebraic functions to calculating the precise gradients used to train modern artificial intelligence models. What you will learn: Understand the foundational concepts of derivatives and rates of change; Master composite functions and trace how inputs flow through multiple layers; Apply the chain rule step-by-step to single-variable and multi-variable equations; Connect calculus directly to neural network training and the backpropagation algorithm; Practice calculating gradients with structured, written exercises designed for coders. You will start with essential mathematical definitions and core terminology before moving on to practical derivative calculations and their direct applications in machine learning algorithms. This course is designed for beginners, software developers, and aspiring data scientists with basic algebra knowledge who want to understand the math behind the code. Start building a rock-solid mathematical foundation for your AI journey today.