Neural networks consist of three main types of layers:
Input Layer – Receives raw data (e.g., images, text, numerical values).
Hidden Layers – Intermediate layers that perform computations and extract patterns. These layers contain neurons (nodes) connected through weights and biases. The deeper the network, the more complex patterns it can learn.
Output Layer – Produces the final result, such as a classification label or prediction value.
Each connection between neurons has a weight that determines its importance. The activation function (such as ReLU, Sigmoid, or Softmax) helps the network decide whether a neuron should be activated based on incoming signals. Through a process called backpropagation, the network learns by adjusting weights to minimize prediction errors.
