Foundations of Neural Networks: Biological Neurons to Activation Functions

Ever wondered how artificial intelligence mimics the human brain? To truly understand deep learning, you must first grasp the biological inspiration and mathematical foundations that power every artificial neuron. In this text-based course, you will journey from the biology of human brain cells to the mathematical formulas of artificial neural networks. You will gain a clear, intuitive grasp of how data flows through a network, how weights and biases shape information, and why activation functions are critical for solving complex, real-world problems. What you will learn: Understand the structural parallels between biological neurons and artificial nodes; Explain the roles of weights, biases, and summation in a basic artificial neuron; Analyze core activation functions including Sigmoid, Tanh, ReLU, and modern variants like GELU; Determine how non-linearity allows neural networks to learn complex patterns; Trace the forward propagation process through written step-by-step calculations. This course starts with foundational definitions and biological concepts before guiding you through the mathematical mechanics of activation functions. You will study clear logic and written exercises to solidify your understanding of how neural layers process information. Designed entirely for beginners, this course requires no prior background in advanced calculus or machine learning. Start reading today to unlock the fundamental mechanics of artificial intelligence.