This post explains gradient descent, a core concept in AI/ML techniques, using a simple analogy of predicting weight based on height. It discusses the training process, the method of least squares, and the use of gradient descent to optimize the model parameters. Additionally, it provides historical context for the term 'regression' in 'linear regression'.
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