Developer builds AI-assisted gradient descent demo to deepen machine learning understanding

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A developer studying fast.ai's machine learning course built an interactive browser demo to solidify their understanding of gradient descent after finding passive reading insufficient. Using Claude as a coding assistant, they created a JavaScript app featuring an 8x8 pixel grid where users draw creatures, label them, and watch a linear model train one image at a time. The demo displays real-time arithmetic — including pixel values, errors, gradients, and weight updates — making the math tangible rather than abstract. Adding a third image class unexpectedly illustrated one-vs-rest classification, helping the developer grasp why each class requires its own weight grid. The author credits the act of precisely specifying the demo's requirements to an AI, not just consuming explanations, as what ultimately cemented their understanding of the concept.

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