Multi-layer Network

A multi-layer neural network is an advanced model used in artificial intelligence and machine learning. Imagine it as a collection of interconnected perceptrons, the topic we talked about in a previous session. However, unlike the perceptron, which only has one layer of neurons, the multi-layer neural network consists of multiple layers stacked on top of each other. Each layer receives input from the previous layer and applies a mathematical operation called an activation function, like the sigmoid function. This allows the network to capture complex relationships between inputs and outputs. To make the network learn, we use back-propagation, a technique that adjusts the weights connecting the neurons based on the error between the predicted and actual output. This adjustment is guided by gradient descent or other algorithms. Through this iterative process, the network improves its ability to make accurate predictions, ultimately enabling it to solve complex problems.

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