Deep learning algorithms operate by mimicking the structure and function of the human brain through interconnected layers of artificial neurons. These algorithms use large datasets to identify patterns and learn representations of data features. Training involves adjusting weights through a process called backpropagation, allowing models to minimize prediction errors. Once trained, these algorithms can perform tasks such as image recognition, natural language processing, and decision-making. This capability makes them highly effective for complex problems in various fields, including healthcare and finance.