Deep Learning
DL/dictionary/dl
Definition
A subset of machine learning that uses neural networks with many layers ("deep" stacks). Powers image recognition, speech, and the LLMs behind ChatGPT/Claude/Gemini. Needs much more data and compute than classical ML, but scales further.
Example
Every modern LLM is a deep-learning model — a transformer with billions of parameters trained on internet-scale text.
Related terms
Posts that use this term
- From models to LLMs
An LLM is one kind of ML model, trained on text, predicts the next token. That single trick at scale gets you ChatGPT, and also explains where it breaks.
- How a model learns: training and inference
Training is the expensive one-time event where a model's numbers get tuned. Inference is the cheap repeated use afterwards. The gap in cost is enormous, and it shapes the whole industry.
- What makes a model: data and algorithm
A model is a file of learned numbers, produced by running an algorithm over data. Both ingredients matter, but bad data beats a good algorithm every time.
- Inside AI: machine learning and deep learning
Open the AI umbrella. Machine learning is the part that learns from data. Deep learning is ML done with neural networks, and that's where today's models live.
- AI, in plain words
What "AI" actually means, where the term came from, and why every product calls itself AI now. Sets up where machine learning and deep learning fit underneath.