ChatGPT and Competing LLMs
Published by Pearson
Compare ChatGPT, BERT, LLAMA, and other LLMs to select the best solution for your application
- You’ll learn how to evaluate and compare existing LLMs
- You’ll gain an understanding of the fundamental concepts and ideas underlying LLMs through intuitive presentation
- You'll explore applications for LLMs that are text-based, such as ChatGPT, LLAMA, and BERT, as well as image- and code-based, such as DALL-E and Codex
Large Language Models (LLMs) are perhaps the largest step forward in natural language processing in recent years. LLMs combine almost inconceivable amounts of textual data, the latest developments in Transformer deep neural networks, and self-supervised machine learning approaches to learn billions of parameters. The end result is a class of systems that is unprecedented in its capability to generate text and interact with users in a way that feels natural.
Over the past 2 to 3 years, a large number of LLMs have been trained by different industry and academic teams and optimized for specific tasks and architectures, such as unidirectional and bidirectional transformers. In this live course, you will learn about the fundamental concepts underlying LLMs and the pros and cons of each approach, and analyze specific models in some detail. Our goal is to provide you with the conceptual framework necessary to understand the latest developments in this area and to quickly evaluate which model might be the right solution for your own specific problem. Practical examples using ChatGPT and the OpenAI API will be used to give attendees a hands-on understanding of the power and limitations of this class of systems.
What you’ll learn and how you can apply it
- Large language models
- Auto-encoders and Transformers
- The different types of LLM architectures
And you’ll be able to:
- Understand the differences between ChatGPT and its main competitors’ models
- Design prompts to customize the output of ChatGPT
- Understand how LLM models can be applied to other classes of problems outside the NLP domain
This live event is for you because...
- You are a senior data scientist or data science manager who is interested in grasping the concepts and technologies underlying the latest developments in large language models.
- You are a beginner in understanding the big picture of how LLMs can be used and tailored for business applications without diving into the idiosyncratic details of specific implementations
Prerequisites
- Natural Language Processing (NLP)
- Basic Python
- Numpy
- Matplotlib
- Jupyter
Course Set-up
- GitHub repo: https://github.com/DataForScience/ChatGPT
- The following applications will be used in the course:
- Python
- Pandas
- Maplotlib
- Jupyter notebooks
- OpenAI (optional)
Recommended Preparation
- Attend: NLP with Deep Learning for Everyone by Bruno Gonçalves
- Read: Language Models in Plain English by Austin Eovito, Marina Danilevsky
- Watch: Natural Language Processing, 2nd Edition by Bruno Goncalves
Recommended Follow-up
- Attend: Generative Artificial Intelligence with the OpenAI API for Developers by Bruno Gonçalves
- Read: What Are ChatGPT and Its Friends? by Mike Loukides
- Read: Quick Start Guide to Large Language Models by Sinan Ozdemir
- Watch: Introduction to Transformer Models for NLP by Sinan Ozdemir
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
Segment 1 – Language Models (50 min)
- Basic Principles
- Statistical Models
- Encoder-Decoder
- Transformer Models
- Q&A
- Break (10 min)
Segment 2 – Large Language Models (50 min)
- ChatGPT Architecture
- BERT Architecture
- LLAMA Architecture
- Model Comparison
- Q&A
- Break (10 min)
Segment 3 – Embeddings (50 min)
- Understanding Embeddings
- Question Answering
- Recommendations
- Long Texts
- Q&A
- Break (10 min)
Segment 4- Applications Outside NLP (50 min)
- CODEX Model
- DALL-E
- BloombergGPT
- BlockGPT
- Final Q&A (10 min)
Your Instructor
Bruno Gonçalves
Bruno Gonçalves is an author, public speaker, corporate trainer, and consultant specializing in Generative AI, Blockchain Analytics, and Machine Learning. He has a diverse background that spans academia and industry, having previously served as a Data Science fellow at NYU's Center for Data Science while on leave from his tenured faculty position at Aix-Marseille Université. Bruno earned his PhD in the Physics of Complex Systems in 2008. He later focused his research on applying Data Science and Machine Learning to the large-scale analysis of online human behavior.