Merge pull request #1224 from qdrant/deeplearning-announcement

[blog] DeepLearning AI Course Announcement

qdrant-landing/content/blog/qdrant-deeplearning-ai-course.md

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+---
+draft: false
+title: "New DeepLearning.AI Course on Retrieval Optimization: From Tokenization to Vector Quantization"
+short_description: "Free, beginner-friendly course to learn retrieval optimization and boost search performance."
+description: "Join Qdrant and DeepLearning.AI’s free, beginner-friendly course to learn retrieval optimization and boost search performance in machine learning."
+preview_image: /blog/qdrant-deeplearning-ai-course/preview.jpg
+social_preview_image: /blog/qdrant-deeplearning-ai-course/preview.jpg
+date: 2024-10-06T00:02:00Z
+author: Qdrant
+featured: false
+tags:
+  - DeepLearning.AI
+  - Vector Search
+  - Vector Quantization
+  - Tokenization
+  - Retrieval-Augmented Generation
+  - 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 [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)](/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.
+
+<iframe width="560" height="315" src="https://www.youtube.com/embed/AE8i69Kcodc?si=IdTEKlUHVbGzgJD-" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
+
+## What You’ll Learn
+
+In this course, you’ll explore key concepts that will enhance your understanding of retrieval optimization:
+
+- 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](/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
+
+This course is tailored for anyone with basic Python knowledge.
+
+Whether you’re starting your journey in machine learning or looking to enhance your existing skills, this course offers valuable insights to boost your capabilities.
+
+### At a Glance:
+
+- **Speaker**: Kacper Łukawski, Qdrant Developer Advocate
+- **Level**: Beginner
+- **Cost**: Free
+- **Location**: Online
+- **Duration**: 1 Hour
+
+## How to Enroll
+
+[Enroll via the DeepLearning.AI website](https://www.deeplearning.ai/short-courses/retrieval-optimization-from-tokenization-to-vector-quantization/?utm_campaign=qdrant-launch&utm_medium=qdrant&utm_source=partner-promo).