What are neural networks in deep learning?

Neural networks are computational models inspired by the structure and function of the human brain, designed to recognize patterns and make decisions from data. At their core, they consist of interconnected layers of artificial neurons (or nodes) that process information. Each neuron receives input, applies a mathematical operation (like a weighted sum), and passes the result through an activation function to produce an output. These networks are organized into layers: an input layer that receives data, hidden layers that transform the data, and an output layer that produces the final prediction. For example, in image classification, pixels are fed into the input layer, and the network outputs a label like “cat” or “dog” after processing through hidden layers.

Training a neural network involves adjusting its weights—parameters that determine how inputs are combined—to minimize prediction errors. This is done using an algorithm called backpropagation, which calculates the gradient of the error with respect to each weight and updates them using optimization techniques like stochastic gradient descent. For instance, when training a network to predict housing prices, the model starts with random weights, compares its predictions to actual prices, and iteratively adjusts the weights to reduce the difference. Activation functions, such as ReLU (Rectified Linear Unit), introduce non-linearity, allowing the network to model complex relationships. Without these functions, even deep networks would behave like linear models, limiting their ability to solve real-world problems.

Neural networks vary in architecture to suit different tasks. Convolutional Neural Networks (CNNs) excel at image processing by using filters to detect spatial patterns like edges or textures. Recurrent Neural Networks (RNNs) handle sequential data (e.g., text or time series) by maintaining a memory of previous inputs through loops. Transformers, widely used in natural language processing, rely on attention mechanisms to weigh the importance of different parts of the input. Developers often use frameworks like TensorFlow or PyTorch to implement these architectures efficiently. For example, a CNN built with PyTorch might use convolutional layers to identify features in medical images, while a transformer model could generate code snippets from natural language descriptions. Understanding these components helps developers choose the right architecture and optimize performance for specific applications.