1. What are neural networks and how do they work?

Neural networks are mathematical models inspired by the structure and function of biological neurons. They consist of layers of neurons (nodes) that process input data and pass it to subsequent layers.

Basic components of a neural network:

1. Neurons (nodes): Basic processing units that receive input signals, process them, and send output signals.
2. Layers: Networks consist of an input layer, hidden layers, and an output layer. Hidden layers learn complex patterns in the data.
3. Weights and biases: Each connection between neurons has an associated weight, and each neuron has a bias value, both of which are adjusted during network training.
4. Activation function: A mathematical function applied to the input signal of a neuron, adding non-linearity to the model.

How neural networks work:

1. Input data is fed into the input layer.
2. Each neuron computes a weighted sum of its inputs and applies an activation function.
3. The results are passed to neurons in the next layer.
4. The process is repeated until the output layer generates the final result.

Neural networks are used in various applications, such as image recognition, natural language processing, and computer games.