Perceptron Learning Algorithm | The Perceptron Learns | Part 3 скачать в хорошем качестве

Perceptron Learning Algorithm | The Perceptron Learns | Part 3

Training a perceptron involves finding weights and a bias that correctly classify training examples. Viewers will learn the perceptron learning algorithm: initialize weights and bias, iterate through training examples, compute the output using the current weights, and update weights when the perceptron misclassifies a sample. The update rule adds the product of the learning rate, the difference between desired and predicted output, and the input vector to the current weights. The algorithm converges for linearly separable data. A simple example (e.g., classifying points above or below a line) can be used to illustrate the learning process