New DeepLearning.AI Course on Retrieval Optimization: From Tokenization to Vector Quantization
Qdrant
·October 06, 2024
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We’re excited to announce a new course on DeepLearning.AI’s platform: Retrieval Optimization: From Tokenization to Vector Quantization. This collaboration between Qdrant and DeepLearning.AI aims to empower developers and data enthusiasts with the skills needed to enhance vector 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.
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 and how to optimize your search by adjusting HNSW parameters and vector quantizations.
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
