Medium.com contributors present a structured overview of Artificial Intelligence, Machine Learning, and Deep Learning by illustrating their nested relationship. The article defines each domain, describes key algorithms, and contrasts data requirements, interpretability, and computational demands, equipping intermediate readers with precise, jargon-light explanations of real-world applications and guiding principles for selecting the appropriate approach in various technological contexts.
Key points
- Defines AI as systems performing human-like tasks across reasoning, perception, and language.
- Outlines ML paradigms—supervised, unsupervised, and reinforcement learning—and their data-driven model training.
- Describes deep learning architectures including CNNs, RNNs, and transformer networks and their applications in unstructured data.
Why it matters: Understanding AI, ML, and DL distinctions empowers strategic tech adoption and innovation across industries.
Q&A
- What exactly distinguishes Machine Learning from traditional rule-based AI?
- Why do Deep Learning models require large datasets?
- What are the main types of neural network architectures in Deep Learning?
