What is Deep Learning?
Deep learning is a specialized subset of machine learning. While basic machine learning algorithms perform well on structured data (like spreadsheets), they struggle with unstructured data like raw images or audio. Deep learning solves this by using artificial neural networks with multiple layers (hence "deep") to progressively extract higher-level features from raw input.
How does it work?
A deep learning model consists of an input layer, an output layer, and multiple "hidden layers" of artificial neurons in between. Data passes sequentially through these layers. In an image recognition model, the first layer might only detect edges and contrasting colors. The next layer combines those edges to detect simple shapes. The final layers combine those shapes to identify complex objects, like a dog's face.
What is a simple example?
If you want to train a system to predict house prices based on square footage and location, standard machine learning is sufficient. If you want a system to transcribe spoken audio into text in real-time despite background noise and varying accents, you must use deep learning.
What is it commonly confused with?
Deep learning is often conflated with machine learning. Think of artificial intelligence as a large circle, machine learning as a smaller circle inside it, and deep learning as an even smaller circle inside machine learning.
Why does it matter?
Deep learning is the technology responsible for the massive AI boom of the 2010s and 2020s. It is the underlying architecture required to build computer vision systems, realistic voice synthesizers, and the Large Language Models that power modern generative AI.