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54 lines
3.5 KiB
Markdown
54 lines
3.5 KiB
Markdown
---
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draft: false
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title: "New DeepLearning.AI Course on Retrieval Optimization: From Tokenization to Vector Quantization"
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short_description: "Free, beginner-friendly course to learn retrieval optimization and boost search performance."
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description: "Join Qdrant and DeepLearning.AI’s free, beginner-friendly course to learn retrieval optimization and boost search performance in machine learning."
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preview_image: /blog/qdrant-deeplearning-ai-course/preview.jpg
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social_preview_image: /blog/qdrant-deeplearning-ai-course/preview.jpg
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date: 2024-10-06T00:02:00Z
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author: Qdrant
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featured: false
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tags:
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- DeepLearning.AI
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- Vector Search
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- Vector Quantization
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- Tokenization
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- Retrieval-Augmented Generation
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- Vector Database
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---
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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.
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Led by Qdrant’s Kacper Łukawski, this free, one-hour course is designed for beginners eager to delve into the world of retrieval optimization.
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## Why This Collaboration Matters
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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.
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<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>
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## What You’ll Learn
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In this course, you’ll explore key concepts that will enhance your understanding of retrieval optimization:
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- Learn how tokenization works in large language and embedding models and how the tokenizer can affect the quality of your search.
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- Explore how different tokenization techniques including Byte-Pair Encoding, WordPiece, and Unigram are trained and work.
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- 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/).
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## Who Should Enroll
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This course is tailored for anyone with basic Python knowledge.
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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.
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### At a Glance:
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- **Speaker**: Kacper Łukawski, Qdrant Developer Advocate
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- **Level**: Beginner
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- **Cost**: Free
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- **Location**: Online
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- **Duration**: 1 Hour
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## How to Enroll
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[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). |