Algorithm

Algorithms form the computational backbone of every AI system, providing the precise step-by-step instructions that transform raw data into predictions, decisions, and insights. In machine learning, algorithms range from simple linear regression and decision trees to complex deep learning architectures involving millions of parameters. The choice of algorithm profoundly affects a model's accuracy, training speed, interpretability, and suitability for a given task. For example, gradient boosting algorithms like XGBoost excel at structured tabular data, while convolutional neural networks dominate image classification, and transformer-based algorithms power modern natural language processing. Beyond model training, algorithms govern optimization through methods like the Adam optimizer and backpropagation, as well as data processing through sorting, search, and clustering techniques. Understanding algorithmic complexity and tradeoffs is fundamental to AI engineering, influencing everything from AutoML systems that automate algorithm selection to the design of efficient inference pipelines for production deployment.