From d51c902e5da059d870f3636cddf781fa43a34974 Mon Sep 17 00:00:00 2001 From: Jael Gu Date: Wed, 18 Sep 2024 13:36:54 +0800 Subject: [PATCH] Add more resources Signed-off-by: Jael Gu --- README.md | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/README.md b/README.md index 5ce14ac..88fa634 100644 --- a/README.md +++ b/README.md @@ -266,3 +266,13 @@ pit_xs_distilled_224 | pnasnet5large | poolformer_m36 resnet101 | resnet101d | resnet152 resnet152d | resnet200d | resnetaa50 resnetblur50 | resnetrs50 + + +# More Resources + +- [How to Get the Right Vector Embeddings - Zilliz blog](https://zilliz.com/blog/how-to-get-the-right-vector-embeddings): A comprehensive introduction to vector embeddings and how to generate them with popular open-source models. +- [What Are Vector Embeddings?](https://zilliz.com/glossary/vector-embeddings): Learn the definition of vector embeddings, how to create vector embeddings, and more. +- [Embedding Inference at Scale for RAG Applications with Ray Data and Milvus - Zilliz blog](https://zilliz.com/blog/embedding-inference-at-scale-for-RAG-app-with-ray-data-and-milvus): This blog showed how to use Ray Data and Milvus Bulk Import features to significantly speed up the vector generation and efficiently batch load them into a vector database. +- [Image Embeddings for Enhanced Image Search - Zilliz blog](https://zilliz.com/learn/image-embeddings-for-enhanced-image-search): Image Embeddings are the core of modern computer vision algorithms. Understand their implementation and use cases and explore different image embedding models. +- [Enhancing Information Retrieval with Sparse Embeddings | Zilliz Learn - Zilliz blog](https://zilliz.com/learn/enhancing-information-retrieval-learned-sparse-embeddings): Explore the inner workings, advantages, and practical applications of learned sparse embeddings with the Milvus vector database +- [An Introduction to Vector Embeddings: What They Are and How to Use Them - Zilliz blog](https://zilliz.com/learn/everything-you-should-know-about-vector-embeddings): In this blog post, we will understand the concept of vector embeddings and explore its applications, best practices, and tools for working with embeddings. \ No newline at end of file