A neural network comprises several key components. Layers, including input, hidden, and output layers, define the network's structure. Each layer consists of neurons connected by weights.
Activation functions, such as ReLU or sigmoid, introduce non-linearity, enabling the network to model complex relationships. The loss function measures prediction errors, guiding the optimization process.
Optimizers, like SGD or Adam, adjust weights to minimize the loss. These components work together to transform input data into meaningful predictions or classifications.