Neural Network Foundations: Mapping Signal Flow and Activations

Have you ever wondered what actually happens inside a neural network when it processes data? To truly understand artificial intelligence, you must first grasp how signals flow through individual nodes and layers. This text-based course demystifies the internal mechanics of neural networks, guiding you step-by-step through the journey of a signal from input to output. You will build a strong mental model of neural computation without getting lost in overwhelming mathematical jargon. What you will learn: Understand foundational AI terminology, including neurons, layers, weights, and biases; Trace the mathematical path of a signal through a basic two-layer network structure; Analyze how modern activation functions like ReLU, Sigmoid, and GELU shape network outputs; Calculate simple forward-pass operations using clear, step-by-step written examples; Explore how weights and biases are adjusted to help networks learn from errors. The course begins with core definitions and basic math concepts before walking you through practical, written calculations of signal propagation. You will read detailed explanations, analyze clear code snippets, and practice with text-based exercises to solidify your understanding. Designed for absolute beginners, this course requires no prior background in advanced mathematics or machine learning. Start reading today to unlock the core mechanics behind modern artificial intelligence.