Large Language Models for Real-World Applications - A Gentle Intro
summary
An accessible, visual introduction to large language models such as GPT and BERT that explains core intuitions behind modern language understanding and generation. Covers practical real-world applications and how developers can start applying these models in business domains, assuming no prior NLP knowledge.
AI-written summary · about
chapters (11)
- 0:00Start
- 1:11Which step used language processing? 3
- 10:36Customer service
- 11:49Text classification
- 15:50Embeddings are numeric representations of text, capturing its meaning
- 19:09State of the art classifiers are most often obtained by finetuning
- 19:53Baseline embeddings are geared for semantic similarity
- 31:14Three ways to build high-performance text classifiers
- 31:39Prompt engineering is newly emerging as a key programming paradigm
- 32:45Reliable generation most often requires multiple generations
- 35:26Concepts embeddings, similarity, plotting prompt engineering, filtering
related talks
51mLarge Language Models for Real-World Applications - A Gentle Intro
30mLarge Language Models and Prompt Engineering for Developers
44mBuilding projects with language models
56mLarge Language Models for Python Specialists
51mTowards a Pragmatic Understanding of Large Language Models
43mBeyond the Hype: A Realistic Look at Large Language Models
50mGenerative language models
15mFuture with Large Language Models