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.