GAN (Generative Adversarial Network)
GAN (Generative Adversarial Network) is two neural networks competing against each other. One generates fake content, the other tries to detect it's fake. They keep pushing each other to improve until the generator creates incredibly realistic output. GANs pioneered AI image generation before diffusion models took over.
Definition
Two neural networks competing against each other. One generates fake content, the other tries to detect it's fake. They keep pushing each other to improve until the generator creates incredibly realistic output. GANs pioneered AI image generation before diffusion models took over.
Related Terms
More AI & Machine Learning Terms
LLM (Large Language Model)
The AI brain behind ChatGPT and similar tools. It's a massive program trained on tons of text that can understand and generate human-like writing. Think of it as autocomplete on steroids.
RAG (Retrieval Augmented Generation)
A technique that lets AI search your documents before answering questions. Instead of just making stuff up, it pulls real info from your data first. This is how you build a chatbot that actually knows your business.
Embeddings
A way to turn words, sentences, or documents into numbers that capture their meaning. Similar concepts get similar numbers, which lets AI find related content even if the exact words don't match.
Fine-tuning
Teaching an existing AI model new tricks by training it on your specific data. It's like hiring someone with general skills, then training them on how your company does things.
Prompt Engineering
The art of writing instructions that get AI to do what you actually want. It's surprisingly important—the same AI can give garbage or gold depending on how you ask.