Artificial Intelligence Programming Practice Exam 2025 - Free AI Programming Practice Questions and Study Guide

Deep learning is a specialized area within machine learning that focuses on algorithms using neural networks containing multiple layers, often referred to as "deep" networks. The architecture of these networks allows them to learn hierarchical representations of data, making them particularly effective for complex tasks such as image and speech recognition, natural language processing, and more. Each layer in a deep learning model extracts different features from the input data, enabling the system to make more sophisticated inferences about the underlying patterns.

This layered approach is one of the key characteristics that distinguishes deep learning from traditional machine learning techniques, which may use more simplistic algorithms. The depth and complexity of deep learning models allow them to capture intricate patterns that would not be feasible with shallow models or traditional programming methods, which often require explicit feature extraction and can struggle with high-dimensional data.

In contrast to decision trees, which focus on branching based on feature value decisions, and to traditional methods that rely on predefined instructions, deep learning's ability to learn directly from raw data sets it apart as a powerful approach in the AI landscape.