Deep learning is a subset of machine learning, which itself is a branch of artificial intelligence (AI). AI refers to systems designed to perform tasks that typically require human-like intelligence, such as reasoning, problem-solving, or perception. Machine learning focuses on algorithms that enable systems to learn patterns from data without explicit programming. Deep learning takes this further by using artificial neural networks with multiple layers (hence “deep”) to model complex patterns in data. In essence, deep learning is one of many tools within the broader AI toolkit, specialized for handling large-scale, high-dimensional data like images, audio, or text.
A key distinction lies in how these approaches handle data. Traditional machine learning often relies on handcrafted features—for example, using edge detectors for image classification or specific keyword counts for text analysis. Deep learning, however, automates feature extraction through its layered architecture. For instance, a convolutional neural network (CNN) in computer vision might learn to detect edges in its first layer, shapes in deeper layers, and object parts in final layers, all without manual intervention. This makes deep learning particularly effective for tasks where raw data is unstructured or too complex for human-designed features, such as translating languages with transformer models like BERT or generating images using diffusion models.
While deep learning has driven significant advancements in AI, it’s not universally applicable. Simpler machine learning models like decision trees or linear regression remain better suited for small datasets or problems with clear interpretability requirements. For example, a developer building a credit scoring system might prefer a random forest model for its transparency over a neural network. Conversely, deep learning excels in scenarios with abundant data and computational resources, such as training self-driving car systems on terabytes of sensor data. Understanding this relationship helps developers choose the right tool: AI defines the goal, machine learning provides the methods, and deep learning addresses specific challenges within those methods.