From the course: Deep Learning: Getting Started
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The perceptron
From the course: Deep Learning: Getting Started
The perceptron
The perceptron is the unit for learning in an artificial neural network. A perceptron represents an algorithm for supervised learning in an artificial neural network. It is one of the earliest and simplest building blocks of neural networks. It is loosely inspired by how a neuron in the human brain works. Multiple inputs are fed into the perceptron, which performs calculations and produces an output. It represents a single cell or node in a neural network. The perceptron combines inputs, weights, and biases to decide what signal to produce. It is conceptually related to logistic regression. Both use weighted inputs to make predictions, especially for classification problems. To derive the formula for the perceptron, we use the logistic regression formula discussed in our earlier video. Here we replace the slope with the weight called W and the intercept with the bias called B. Weights and biases become the parameters for a neural network. We then apply an activation function f that…
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