Deep learning refers to a subset of machine learning techniques that employ artificial neural networks characterized by multiple layers of interconnected nodes (or neurons) followed by nonlinear activation functions. Such networks are of significant size. These deep neural networks are designed to process and learn from experience, extracting complex patterns and features through successive layers of computation. Such experience can be obtained either from human-annotated electronic records such as datasets or from the learner’s own interactions with its perceived environment. By leveraging these layered architectures, deep learning models can autonomously discover and represent intricate relationships within the data, enabling them to make sophisticated predictions and informed decisions based on learned knowledge.