The key components of a neural network are:
- Input layer: The input layer receives the input data and sends it to the next layer.
- Hidden layers: The hidden layers process the input data and send it to the next layer.
- Output layer: The output layer produces the final output of the network.
- Activation functions: Activation functions are used to introduce non-linearity into the network, allowing it to learn complex relationships between features.
- Weights and biases: Weights and biases are used to adjust the strength of the connections between nodes and the output of the network.