Create your own LLM

Join this hands-on session to learn how to build a large language model (LLM) from scratch using PyTorch. Designed for developers with some knowledge of Python and a basic understanding of deep learning, this workshop offers a deep dive into constructing a ChatGPT-like model. Participants will explore key steps including data preprocessing, implementing attention mechanisms, understanding transformer architectures, and performing both pretraining and fine-tuning. By the end of the session, attendees will have the practical skills to develop and customize LLMs for various applications, from text generation to classification. This session is ideal for those eager to understand the mechanics of LLMs and apply that knowledge to real-world projects.

Creating a Choose Your Own Adventure game with OpenAI

Learn to use OpenAI’s ChatGPT and DALL·E APIs with Python: set up your environment, code API calls, and secure keys. Create an interactive Choose Your Own Adventure game with dynamic text from ChatGPT and visuals from DALL-E.

Enhancing Acceptance Criteria with ChatGPT: From User Stories to Clearer Requirements

In this conference session, we will explore how to leverage ChatGPT to improve the quality of acceptance criteria derived from user stories. Acceptance criteria are essential in Agile and Scrum methodologies, as they define the conditions that must be met for a user story to be considered complete. By using ChatGPT, we can streamline the process of creating acceptance criteria that are more precise, comprehensive, and aligned with stakeholders' expectations.

Introduction to RAG with LangChain and OpenAI

Join us for an exciting deep dive into Retrieval-Augmented Generation (RAG), a powerful technique that's revolutionizing AI applications.

What we'll cover:
* Introduction to RAG: Understanding the basics and benefits
* LangChain overview: A versatile framework for language model applications
* Integrating OpenAI's models with RAG
* Building a Study Revision Aid for College Students

Our main example will showcase how to create a personalized study assistant that can:
* Ingest and understand course materials
* Answer specific questions about the content
* Generate practice quizzes
* Provide explanations tailored to individual learning styles

This meetup is perfect for developers, data scientists, and AI enthusiasts looking to enhance their knowledge and skills in building more intelligent and context-aware AI systems.