Understanding the Perceptron: The Foundation of Neural Networks 🤖

The perceptron is the unit for learning in an artificial neural network. A perceptron represents the algorithm for supervised learning in an artificial neural network. It resembles a human brain cell. Multiple inputs are fed into the perceptron, which in turn does computations and outputs a boolean variable. It represents a single cell or node in a neural network. It is built based on logistic regression. To derive the formula for the perceptron, we use the logistic regression formula discussed in the earlier video. Here, we replace the slope, a, with a weight called w, and intercept b with the bias called b. Weights and biases become the parameters for a neural network. We then apply an activation function f that outputs a boolean result based on the values. This formula for the perceptron is fundamental to deep learning. The same perceptron is shown here in a figure. We have multiple independent input variables, x1 to xn, that are fed to the perceptron. Each of them is multiplied by a corresponding weight. The number of weights equal the number of inputs. We also feed in a 1 that is multiplied by the bias. All the results are then summed up. An activation function is applied, that delivers the value of y, which is either a 1 or a 0.

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