Deep learning is a subset of AI and machine learning that uses multi-layered artificial neural networks to deliver high accuracy in tasks like object detection, speech recognition, and language translation.
Think of it as a more advanced, more autonomous version of regular machine learning. Where traditional machine learning often needs humans to tell it what features to look for in data, deep learning figures that out by itself — directly from raw data like images, video, or text.
How is it different from regular machine learning?
Traditional machine learning requires humans to hand-code rules or define the important features of data. Deep learning skips that step entirely — its flexible architecture learns those features automatically, and the more data you give it, the more accurate it gets.
A simple analogy: Imagine teaching a child to recognize cats. Traditional ML = you explicitly tell them "look for pointy ears, whiskers, and fur." Deep learning = you just show them thousands of cat photos and they figure out the distinguishing features themselves.
Where is it used?
Real-world breakthroughs powered by deep learning:
Deep learning models are trained using large datasets fed through a deep learning framework which is software that helps you build, train, and run neural networks. This process can normally take days or even weeks — but using GPUs (graphics processing units, the chips NVIDIA makes) dramatically speeds this up to hours or days instead.