Explain how a neural network works

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Neural networks are a type of machine learning algorithm inspired by the human brain, consisting of interconnected nodes that process information.

Neural networks consist of layers of interconnected nodes, each node performing a mathematical operation on the input data.
The output of each node is passed through an activation function to introduce non-linearity into the network.
Neural networks learn by adjusting the weights of connections between nodes during training, using algorithms like backpropagation.
Deep learning is a type of neural network with multiple hidden layers, allowing for more complex patterns to be learned.
Examples of neural networks include image recognition, natural language processing, and autonomous driving systems.

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