Deep learning is a specialized branch of machine learning that uses artificial neural networks with many layers to learn complex patterns from large amounts of data. The "deep" in deep learning refers to the depth of these networks — they can have dozens or even hundreds of layers stacked together.
While traditional machine learning often requires humans to manually select which features matter (like telling a system to look at color, shape, and size when identifying objects), deep learning figures out the important features automatically. This is its superpower. Given enough data, a deep learning model can discover patterns that humans might never think to look for.
Deep learning drives most of the AI breakthroughs you've heard about in recent years:
Computer vision: Deep learning models can identify objects in images, detect cancer in medical scans, and power facial recognition. Google Photos can search your pictures by what's in them thanks to deep learning.
Natural language processing: ChatGPT, Claude, and other language models are built on deep learning architectures called transformers. They can understand and generate human language with remarkable fluency.
Speech recognition: Siri, Alexa, and Google Assistant all use deep learning to convert your voice into text and understand your intent.
Generative AI: Image generators like DALL-E and Midjourney use deep learning to create new images from text descriptions.
The tradeoff with deep learning is that it requires significantly more data and computational power than traditional ML. Training GPT-4 cost an estimated $100 million in compute. But for many applications, pre-trained models are available that you can use or fine-tune with much smaller datasets.
Deep learning models are also notoriously difficult to interpret — they're often called "black boxes" because it's hard to understand exactly why they made a specific decision. This is an active area of research, especially for high-stakes applications in healthcare and finance.
The field has exploded since 2012 when a deep learning model called AlexNet dramatically outperformed traditional approaches in image recognition. Since then, architectures have evolved rapidly — from CNNs for images to RNNs for sequences to transformers that now dominate both language and vision tasks.