A leading ML educator on what you need to know about LLMs
Machine learning scientist, author, and LLM developer Maxime Labonne talks with Ben and Ryan about his role as lead machine learning scientist, his contributions to the open-source community, the value of retrieval-augmented generation (RAG), and the process of fine-tuning and unfreezing layers in LLMs. The team talks through various challenges and considerations in implementing GenAI, from data quality to integration.
Check out Maxime’s three-part LLM course.
Part 1 “covers essential knowledge about mathematics, Python, and neural networks.
Part 2 “focuses on building the best possible LLMs using the latest techniques."
Part 3 “focuses on creating LLM-based applications and deploying them.”
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Nikhil Wagh earned a Lifeboat badge by explaining how to Efficiently compare two sets in Python.