Gradient descent is a fundamental optimization algorithm in machine learning. It's a way for models to learn from data and improve their accuracy by gradually adjusting their internal settings. Think of it like carefully descending a hill to find the lowest point—each small step you take brings you closer to the best possible solution.

Explaining Gradient Descent in Machine Learning with a simple analogy

An exploration of the cyclical nature of AI development, tracing the rise and fall of new technologies within the field, and how this pattern has repeated throughout history.

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