GDG Cloud Budapest

Dear Members,

Are you ready to learn about the building blocks of Large Language Models (LLMs)? Join us online on January 23rd and dive into the core tech behind the magic of AI with us! 🚀

Attendance is free of charge but registration is required

💡 Abstract

The goal of this presentation is to give you a brief technical overview on the basics of AI technologies which are used by the current LLM models.

We would look at how Neural networks work and how to use them in solutions, how LLMs transform text into numbers which represents meaning (tokenization and vectorization), what are the capabilities of advanced neural networks like RNN, GRU or CNN which are needed for the Large Language models what were the milestones of the development of these. We would like to show some real-life demos which helps you understand how these technologies can be used.

Also, we would briefly look at the current AI tools on GCP and some examples of how to use technologies like advanced prompt engineering real life Enterprise AI projects.

As you can see, we would like to cover a lot of subjects during an hour, so we will not be able to dig down to the details, but hopefully you will have an overview level understanding of how AI technologies work, what you can explore further later on.

👨‍💻Suggested audience: You are at technical level, but not an expert in AI technologies. You heard a lot about AI, but not familiar with the meaning of some AI expressions like Tokenization, Vectorization, LLM etc. and do not fully understand how AI works.

🗣️ Language: English

👉 Save the date, and don’t miss out on this opportunity to learn, share, and innovate together!

🗓️ Agenda

• Timeline of LLM technology development
• Neural networks basics and Evolution of Advanced Neural Networks (ANN, DNN, RNN, LTSM, GRU, CNN) and enabling technologies (e.g. Tensorflow)
• Natural Language Processing technologies evolution and overview (tokenization, vectorization, encoder-decoder, attention mechanism, transformers)
• Google AI tools in GCP – overview (development frameworks, APIs, Custom training capabilities)
• Enterprise AI project challenges and solutions (Using Enterprise data in AI, Advanced Prompt engineering, manage hallucinations)
• Interactive Q&A

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