Trace how connected artificial neurons combine inputs, transform signals, and learn useful representations.
An accessible four-lesson introduction to neural networks, moving from the artificial neuron to layered networks, activation functions, and learned representations. The course uses conceptual explanations rather than requiring code or calculus. Learners finish by drawing a tiny network and explaining why its outputs still need testing on examples beyond those used to fit it.
Sketch an artificial neuron as a weighted combination of inputs followed by a transformation. Explain how layers connect simple computations into a network. Describe the role of an activation function without treating it as a biological neuron. Explain why a network's learned internal patterns and predictions must be evaluated on held-out examples.
No programming, calculus, or prior AI study is required. Comfort with simple addition and multiplication is useful but not essential.
Beginners who want a clear, guided introduction without unnecessary jargon
People who learn best through examples, guided lessons, and hands-on practice
Professionals, creators, and independent builders looking for a repeatable workflow
Lesson 1 is free
4 lessons · Beginner · Full course.
This course uses CC BY licensed material attributed to its content provider or uploader. Lesson playback is available only through authenticated access after publication and licensing checks pass.