Key takeaways
- Andrew Ng's new free course focuses on vector databases and their applications in generative AI.
- Vector databases enhance LLMs by providing access to real-time proprietary data for RAG applications.
- The course covers techniques for forming embeddings, fast searches, and multilingual search applications.
Andrew Ng, the godfather of deep learning, has come up with “Vector Databases: from Embeddings to Applications”, a new free course on vector databases, their applications, and how they can be used to develop generative AI applications without training or fine-tuning an LLM.
Vector databases play a crucial role in various fields, such as natural language processing, image recognition, recommender systems, and semantic search, and their importance has grown with the increasing adoption of LLMs. They provide LLMs with access to real-time proprietary data, enabling the development of Retrieval Augmented Generation (RAG) applications.
At their core, vector databases rely on embeddings to capture the meaning of data and determine the similarity between different pairs of vectors. The course aims to help learners gain the knowledge to make informed decisions about when to apply vector databases to their applications.
The key topics that will be covered in this course include using vector databases and LLMs to gain deeper insights into data, building labs that demonstrate how to form embeddings using various search techniques to find similar embeddings and exploring algorithms for fast searches through vast datasets and building applications ranging from RAG to multilingual search.
Hallucinations in LLMs have been a persistent issue causing inaccurate and misleading outputs. Researchers have been exploring various solutions to address this problem, but the use of vector databases has shown promise in reducing the risk of hallucination.
Read more: Andrew OG of AI
