Deep Learning
Related Terms
Artificial Intelligence (AI)
The field of computer science focused on building systems that can perform tasks typically requiring human intelligence, such as understanding language, recognizing patterns, and making decisions. The term was coined in 1956 at the Dartmouth Workshop.
Machine Learning
A subset of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. The term was coined by Arthur Samuel in the 1950s while building a checkers program at IBM.
Neural Network
A computing system inspired by the biological neural networks in the brain. First modeled mathematically by McCulloch and Pitts in 1943, neural networks are the foundation of modern deep learning systems.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. Modern NLP is powered by Transformer-based models like GPT and Claude.
Large Language Model (LLM)
An AI model trained on massive amounts of text data that can understand and generate human language. Examples include GPT-4, Claude, and Gemini. These models use the Transformer architecture and typically have billions of parameters.
Generative AI
AI systems that can create new content, including text, images, code, music, and video. The generative AI explosion began around 2022 with tools like ChatGPT, DALL-E, Stable Diffusion, and Midjourney.
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