LinkedIn – Foundational Math for Generative AI: Understanding LLMs and Transformers through Practical Applications 2025-2

Foundational Math for Generative AI: Understanding LLMs and Transformers through Practical Applications. This course will help you gain a deep understanding of the generative AI models used in today’s most advanced AI applications. Rather than just learning how to use large language models like BERT or GPT, we’ll delve into the mathematical foundations of these models. The course starts by teaching sentiment analysis using simple recurrent neural networks (RNNs) and then deepens your understanding by exploring attention mechanisms, transformers, and more advanced models.

What you will learn:

• Mathematics and Generative Artificial Intelligence: Understand the connection between mathematical concepts and generative artificial intelligence.
• Large Language Models: Get to know large language models like BERT and GPT and understand how they work.
• Attention Mechanisms: Understand the importance of attention mechanisms in natural language processing.
• Transformers: Learn about the architecture of transformers and understand why they are superior to RNNs.
• Fine-tuning models: Learn methods for fine-tuning pre-trained models.
• Advanced techniques: Learn about new techniques like LoRA.

Who is this course suitable for?

• This course is suitable for people who want to gain a deep understanding of the mathematical foundations of generative AI and be able to build more efficient AI models. It will also be useful for people who want to work in the field of natural language processing and machine learning.