Generative AI Tutorial – Slide 13
Understanding model fine‑tuning, embedding concepts, and how generative models learn to create new outputs.
Understanding model fine‑tuning, embedding concepts, and how generative models learn to create new outputs.
Slide 13 introduces the idea of how generative AI models use embeddings and learned representations to understand and create content. It explains the relationship between input data, tokenization, vector embeddings, and how models map these into meaningful patterns.
Text is split into tokens that represent words or characters before processing.
Tokens are transformed into numerical vectors representing meaning and relationships.
A multidimensional space where the model stores learned concepts and patterns.
User text or data enters the model.
Data is split into small units for computational processing.
Tokens are converted to vectors representing meaning.
Model uses patterns in vector space to produce new content.
Writing assistance, story generation, marketing copy, and brainstorming tools.
Models generate visuals, art, music, and synthetic voices from embeddings.
Embedding-based search enables semantic lookup beyond keywords.
Models map user behavior into vector profiles for recommendations.
They allow AI models to understand relationships and generate context-aware outputs.
Yes, high-dimensional vectors can encode emotions, styles, categories, and more.
They help models generalize, leading to more natural and coherent output.
Explore more slides, tutorials, and hands‑on examples to deepen your understanding.
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