Deep Learning - Crash Course 2023 - Why Update Rule Works

The video tutorial explains how to update the parameter W in a perceptron model by checking if inputs belong to positive or negative sets and adjusting weights accordingly. It delves into the mathematics of perceptron models, focusing on vector representation and conditions for output. The tutorial also covers the role of cosine values and angles between vectors in predictions, and how weight updates can lead to correct predictions. Finally, it assures viewers of the convergence of this method, encouraging further exploration of the proof.

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