From a575a1c17e42d695f0799c4f7256f1ccfacf729c Mon Sep 17 00:00:00 2001 From: davidmyriel Date: Mon, 7 Oct 2024 12:25:14 -0700 Subject: [PATCH] add seo --- .../content/blog/qdrant-deeplearning-ai-course.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/qdrant-landing/content/blog/qdrant-deeplearning-ai-course.md b/qdrant-landing/content/blog/qdrant-deeplearning-ai-course.md index 891ae3c82..296f29b19 100644 --- a/qdrant-landing/content/blog/qdrant-deeplearning-ai-course.md +++ b/qdrant-landing/content/blog/qdrant-deeplearning-ai-course.md @@ -17,13 +17,13 @@ tags: - Vector Database --- -We’re excited to announce a new course on DeepLearning.AI's platform: [Retrieval Optimization: From Tokenization to Vector Quantization](https://www.deeplearning.ai/short-courses/retrieval-optimization-from-tokenization-to-vector-quantization/?utm_campaign=qdrant-launch&utm_medium=qdrant&utm_source=partner-promo). This collaboration between Qdrant and DeepLearning.AI aims to empower developers and data enthusiasts with the skills needed to enhance search capabilities in their applications. +We’re excited to announce a new course on DeepLearning.AI's platform: [Retrieval Optimization: From Tokenization to Vector Quantization](https://www.deeplearning.ai/short-courses/retrieval-optimization-from-tokenization-to-vector-quantization/?utm_campaign=qdrant-launch&utm_medium=qdrant&utm_source=partner-promo). This collaboration between Qdrant and DeepLearning.AI aims to empower developers and data enthusiasts with the skills needed to enhance [vector search](/advanced-search/) capabilities in their applications. Led by Qdrant’s Kacper Łukawski, this free, one-hour course is designed for beginners eager to delve into the world of retrieval optimization. ## Why This Collaboration Matters -At Qdrant, we believe in the power of effective search to transform user experiences. Partnering with DeepLearning.AI allows us to combine our cutting-edge vector search technology with their educational expertise, providing learners with a comprehensive understanding of how to build and optimize Retrieval-Augmented Generation (RAG) applications. This course is part of our commitment to equip the community with practical skills that leverage advanced machine learning techniques. +At Qdrant, we believe in the power of effective search to transform user experiences. Partnering with DeepLearning.AI allows us to combine our cutting-edge vector search technology with their educational expertise, providing learners with a comprehensive understanding of how to build and optimize [Retrieval-Augmented Generation (RAG)](/rag/rag-evaluation-guide/) applications. This course is part of our commitment to equip the community with practical skills that leverage advanced machine learning techniques. @@ -33,7 +33,7 @@ In this course, you’ll explore key concepts that will enhance your understandi - Learn how tokenization works in large language and embedding models and how the tokenizer can affect the quality of your search. - Explore how different tokenization techniques including Byte-Pair Encoding, WordPiece, and Unigram are trained and work. -- Understand how to measure the quality of your retrieval and how to optimize your search by adjusting HNSW parameters and vector quantizations. +- Understand how to [measure the quality of your retrieval](/rag/rag-evaluation-guide/) and how to optimize your search by adjusting HNSW parameters and [vector quantizations](/articles/what-is-vector-quantization/). ## Who Should Enroll