Generative AI: Working with Large Language Models
1h 22mAdvanced2025-03-17
Authors

Jonathan Fernandes
Consultant focusing on data science, AI, and big data
Course details
Transformers have quickly become the go-to architecture for natural language processing (NLP). As a result, knowing how to use them is now a business-critical skill in your AI toolbox. In this course, instructor Jonathan Fernandes walks you through many of the key large language models developed since GPT-3. He presents a high-level overview of GLaM, Megatron-Turing NLG, Gopher, Chinchilla, PaLM, OPT, and BLOOM, relaying some of the most important insights from each model.
Get a high-level overview of large language models, where and how they are used in production, and why they are so important to NLP. Additionally, discover the basics of transfer learning and transformer training to optimize your AI models as you go. By the end of this course, you’ll be up to speed with what’s happened since OpenAI first released GPT-3 as well as the key contributions of each of these large language models.
Get a high-level overview of large language models, where and how they are used in production, and why they are so important to NLP. Additionally, discover the basics of transfer learning and transformer training to optimize your AI models as you go. By the end of this course, you’ll be up to speed with what’s happened since OpenAI first released GPT-3 as well as the key contributions of each of these large language models.
Skills covered
Natural Language Processing (NLP)Generative AIAdvancedArtificial Intelligence (AI)
Concepts
Introduction
- Learning about Large Language Models
Transformers in NLP
- What are large language models
- Transformers in production
- Transformers - History
Training Transformers and Their Architecture
- Transfer learning
- Transformer - Architecture overview
- Self-attention
- Multi-head attention and Feed Forward Network
Large Language Models
- GPT-3
- GPT-3 use cases
- Challenges and shortcomings of GPT-3
- GLaM
- Megatron-Turing NLG Model
- Gopher
- Scaling laws
- Chinchilla
- BIG-bench
- PaLM
- OPT and BLOOM
- GitHub models
- Accessing Large Language Models using an API
- Inference time vs. pre-training
Conclusion
- Going further with Transformers