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Welcome to Episode 1 of the Bill's Guide to A.I series! In this introductory episode, Bill Kennedy dives into the core concepts of vector databases and embeddings, providing a clear explanation of how these foundational AI tools function. Through easy-to-follow examples, Bill demonstrates how vector databases organize data and how embeddings help AI systems find relationships between different data points. Throughout this episode, Bill explores how vector embeddings allow AI models to compare and analyze data by plotting it in multi-dimensional space. He walks through examples like comparing similarities between kings, queens, and other figures to explain how cosine similarity helps measure these relationships. By laying out the basics of vector databases, Bill sets the stage for more complex AI applications, giving viewers a strong foundation to build upon. This episode is perfect for anyone starting their journey into AI or looking to better understand how vector databases and embeddings are used in machine learning models. Bill's approachable style makes these complex topics easy to grasp, setting up an exciting series on AI development. What you will learn in this video: The concept of vector embeddings and how they represent data in multi-dimensional space to identify similarities. How cosine similarity works to measure the relationship between data points and find connections in a vector database. The importance of vector databases in enhancing AI models by enabling efficient data retrieval and analysis. Comment below or tweet us on twitter & let us know your thoughts, we want to hear from you! ~ If you found this video helpful, hit that like button & subscribe for more content like this. ---- Access our online courses → https://www.ardanlabs.com/education/ Attending a live training → https://www.ardanlabs.com/live-traini... Other Links: Website: https://www.ardanlabs.com/ Github: https://github.com/ardanlabs Twitter: / ardanlabs #A.I #programming #education #tutorials #tips