How are artificial neural networks used in machine learning?

Artificial neural networks (ANNs) are computational models inspired by biological neurons, used in machine learning to identify patterns and make decisions from data. They consist of interconnected layers of nodes (neurons) that process inputs through weighted connections. Each node applies an activation function to its inputs, passing results to the next layer. For example, in image recognition, an ANN might process pixel values through hidden layers to detect edges, shapes, and eventually objects. This layered structure allows ANNs to learn hierarchical representations, making them effective for complex tasks like classification or regression.

Training ANNs involves adjusting connection weights to minimize prediction errors. This is done using algorithms like backpropagation, which calculates gradients of a loss function (e.g., mean squared error) with respect to the weights. Optimization methods like stochastic gradient descent update weights iteratively using training data. For instance, in training a network to classify handwritten digits (MNIST dataset), the model adjusts its weights to reduce misclassifications over multiple epochs. Techniques like dropout or batch normalization are often added to prevent overfitting, ensuring the model generalizes well to unseen data. Frameworks like TensorFlow or PyTorch automate these steps, letting developers focus on architecture design.

ANNs are applied across domains, often tailored to specific data types. Convolutional neural networks (CNNs) excel in computer vision by using filters to capture spatial hierarchies, as seen in self-driving cars for object detection. Recurrent neural networks (RNNs), with their memory-like structure, handle sequential data like text or time series, enabling tasks such as language translation. Transformers, a newer architecture, use attention mechanisms for context-aware processing, powering models like BERT for NLP. In reinforcement learning, ANNs act as policy or value estimators, enabling systems like game-playing agents (e.g., AlphaGo). These applications demonstrate how ANN architectures are adapted to leverage domain-specific patterns efficiently.