We hear the terms AI, Machine Learning, and Deep Learning all the time, but they’re not interchangeable. And if you’ve ever felt a bit lost between them, you’re definitely not alone.
Here’s a quick breakdown to help clear things up:
Artificial Intelligence (AI) is the broadest concept. It refers to any technique that enables machines to mimic human intelligence, whether that’s answering questions, recognizing faces, recommending content, or planning routes. It's the umbrella term that covers everything from rule-based systems to generative models.
Machine Learning (ML) is a subset of AI. Instead of manually programming rules, we train machines with data so they can learn patterns and make predictions. Think spam filters, recommendation engines, or fraud detection, ML learns from past behavior to predict future outcomes.
Deep Learning (DL) is a specialized subset of ML. It uses neural networks—layered architectures inspired by the human brain to solve more complex problems, especially when working with unstructured data like images, audio, or natural language. DL powers things like facial recognition, language translation, and large language models like GPT.
We made a short video explainer that illustrates the differences in a quick and visual way. If you're just getting started or want to explain it clearly to someone else, it’s a great reference point.
Always interested in hearing how others are applying these in real-world projects.
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