AI & LLM Expert | Author | Founder + CTO at LoopGenius
Sinan Ozdemir is a mathematician, data scientist, NLP expert, lecturer, and accomplished author. He is currently applying my extensive knowledge and experience in AI and Large Language Models (LLMs) as the founder and CTO of LoopGenius, transforming the way entrepreneurs and startups market their products and services. Simultaneously, he is providing advisory services in AI and LLMs to Tola Capital, an innovative investment firm. He has also worked as an AI author for Addison Wesley and Pearson, crafting comprehensive resources that help professionals navigate the complex field of AI and LLMs. Previously, he served as the Director of Data Science at Directly, where my work significantly influenced their strategic direction. As an official member of the Forbes Technology Council from 2017 to 2021, he shared his insights on AI, machine learning, NLP, and emerging technologies-related business processes. He holds a B.A. and an M.A. in Pure Mathematics (Algebraic Geometry) from The Johns Hopkins University, and he is an alumnus of the Y Combinator program. Sinan actively contribute to society through various volunteering activities. Sinan's skill set is strongly endorsed by professionals from various sectors and includes data analysis, Python, statistics, AI, NLP, theoretical mathematics, data science, function analysis, data mining, algorithm development, machine learning, game-theoretic modeling, and various programming languages.
All Sessions by Sinan Ozdemir
Aligning Open-source LLMs Using Reinforcement Learning from FeedbackGenerative AI | Intermediate-Advanced
Unlock the full potential of open-source Large Language Models (LLMs) in our alignment workshop focused on using reinforcement learning (RL) to optimize LLM performance. With LLMs like ChatGPT and Llama-2 revolutionizing the field of AI, mastering the art of fine-tuning these models for optimal human interaction has become crucial. Throughout the session, we will focus on the core concepts of LLM fine-tuning, with a particular emphasis on reinforcement learning mechanisms. Engaging in hands-on exercises, attendees will gain practical experience in data preprocessing, quality assessment, and implementing reinforcement learning techniques for manual alignment. This skill set is especially valuable for achieving instruction-following capabilities and much more. The workshop will provide a comprehensive understanding of the challenges and intricacies involved in aligning LLMs. By learning to navigate through data preprocessing and quality assessment, participants will gain insights into identifying the most relevant data for fine-tuning LLMs effectively. Moreover, the practical application of reinforcement learning techniques will empower attendees to tailor LLMs for specific tasks, ensuring enhanced performance and precision in real-world applications. By the workshop's conclusion, attendees will be well-equipped to harness the power of open-source LLMs effectively, tailoring their models to meet the specific demands of their industries or domains. Don't miss out on this opportunity to learn how to create your very own instruction-aligned LLM and enhance your AI applications like never before!