Neural networks are computational models designed to recognize patterns by processing data through interconnected layers of nodes. Each node, or neuron, applies a mathematical operation to its input and passes the result to the next layer. The network typically consists of an input layer (which receives raw data), one or more hidden layers (which transform the data), and an output layer (which produces a prediction or classification). For example, in image recognition, pixels from an image are fed into the input layer, hidden layers detect edges or textures, and the output layer might identify the object in the image. The connections between neurons have weights, which adjust during training to improve accuracy.
Training a neural network involves two key steps: forward propagation and backpropagation. During forward propagation, input data passes through the network, and each layer’s output becomes the next layer’s input. The final output is compared to the correct result using a loss function, which quantifies the error. Backpropagation then calculates how much each weight contributed to the error by working backward through the network. This is done using gradient descent, an optimization algorithm that adjusts weights to minimize the loss. For instance, if a network misclassifies a cat image as a dog, backpropagation identifies which neurons caused the mistake and updates their weights to reduce the likelihood of repeating it.
Practical implementation requires careful tuning of hyperparameters like learning rate (how quickly weights adjust) and the number of hidden layers. Activation functions, such as ReLU (Rectified Linear Unit), introduce non-linearity, allowing the network to model complex relationships. For example, ReLU outputs zero for negative inputs and the input value otherwise, enabling the network to ignore irrelevant data. Overfitting—when a model performs well on training data but poorly on new data—is mitigated using techniques like dropout, which randomly disables neurons during training. Developers often use frameworks like TensorFlow or PyTorch to handle these details, letting them focus on designing architectures suited to specific tasks, such as convolutional networks for images or transformers for text.