A neural network consists of three core components: layers, activation functions, and a loss function paired with an optimizer. Layers are structured as interconnected nodes (neurons) that process data. Activation functions introduce non-linearity, enabling the network to model complex patterns. The loss function quantifies prediction errors, and the optimizer adjusts the network’s parameters (weights and biases) to minimize those errors. Together, these elements form the foundation for training and inference in neural networks.
Layers are the building blocks. The input layer receives raw data (e.g., pixel values for images), hidden layers transform the data through weighted connections, and the output layer produces predictions (e.g., classification probabilities). Each neuron in a layer computes a weighted sum of its inputs, adds a bias term, and passes the result through an activation function. For example, a simple network for digit recognition might use a 784-node input layer (for 28x28 pixel images), two hidden layers with ReLU activation, and a 10-node output layer with softmax for class probabilities. Weights and biases are learned parameters that determine how signals propagate through the network.
The loss function (e.g., mean squared error for regression or cross-entropy for classification) measures how far predictions deviate from actual targets. The optimizer (like stochastic gradient descent or Adam) uses gradients calculated via backpropagation to update weights and biases. For instance, in training a text classifier, cross-entropy loss compares predicted word probabilities to true labels, and Adam adjusts parameters to reduce this loss over iterations. Activation functions like ReLU or sigmoid ensure the network can model non-linear relationships—without them, the network would collapse into a linear model. These components interact during training: data flows forward, loss is computed, gradients flow backward, and the optimizer iteratively improves performance.