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Vector Databases

from Embeddings to Applications

Sebastian Witalec

Vector databases play a pivotal role across various fields, such as natural language processing, image recognition, recommender systems and semantic search, and have gained more importance with the growing adoption of LLMs.

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Vector databases play a pivotal role across various fields, such as natural language processing, image recognition, recommender systems and semantic search, and have gained more importance with the growing adoption of LLMs.

These databases are exceptionally valuable as they provide LLMs with access to real-time proprietary data, enabling the development of Retrieval Augmented Generation (RAG) applications.

At their core, vector databases rely on the use of embeddings to capture the meaning of data and gauge the similarity between different pairs of vectors and sift through extensive datasets, identifying the most similar vectors.

This course will help you gain the knowledge to make informed decisions about when to apply vector databases to your applications. You’ll explore:

1. How to use vector databases and LLMs to gain deeper insights into your data.

2. Build labs that show how to form embeddings and use several search techniques to find similar embeddings.

3. Explore algorithms for fast searches through vast datasets and build applications ranging from RAG to multilingual search.

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What's inside

Syllabus

Project Overview
Vector databases play a pivotal role across various fields, such as natural language processing, image recognition, recommender systems and semantic search, and have gained more importance with the growing adoption of LLMs. These databases are exceptionally valuable as they provide LLMs with access to real-time proprietary data, enabling the development of Retrieval Augmented Generation (RAG) applications.At their core, vector databases rely on the use of embeddings to capture the meaning of data and gauge the similarity between different pairs of vectors and sift through extensive datasets, identifying the most similar vectors. This course will help you gain the knowledge to make informed decisions about when to apply vector databases to your applications. You’ll explore: (1) How to use vector databases and LLMs to gain deeper insights into your data. (2) Build labs that show how to form embeddings and use several search techniques to find similar embeddings. (3) Explore algorithms for fast searches through vast datasets and build applications ranging from RAG to multilingual search.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Explores the rapidly growing field of vector databases and their applications
Taught by leading expert Sebastian Witalec
Provides a comprehensive overview of vector database concepts and techniques
Covers advanced techniques such as Retrieval Augmented Generation (RAG)
Suitable for intermediate to advanced learners in data science or machine learning
Requires prior knowledge of natural language processing and machine learning

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Vector Databases: from Embeddings to Applications with these activities:
Compile a list of resources on vector databases
Expand your knowledge by compiling a list of resources that provide further insights into vector databases.
Browse courses on Vector Databases
Show steps
  • Conduct online research
  • Gather information from books and articles
  • Organize and categorize the resources
Review relevant vector databases concepts
Refresh your understanding of vector databases concepts to strengthen the foundation for this course.
Browse courses on Vector Databases
Show steps
  • Read through your old course notes
  • Review online resources
Review Data Structures and Algorithms
Solidify your understanding of basic data structures and algorithms, ensuring a solid foundation for working with vector databases.
Browse courses on Data Structures
Show steps
  • Review online resources on data structures (e.g., arrays, linked lists, trees).
  • Try out coding problems that focus on implementing basic data structures.
  • Review time and space complexity analysis for common algorithms.
Seven other activities
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Practice using vector databases
Strengthen your skills in using vector databases through practical exercises.
Browse courses on Vector Databases
Show steps
  • Complete the labs provided in the course
  • Find additional online exercises
Attend Meetups or Conferences on Vector Databases
Connect with professionals in the field, broaden your network, and stay updated on industry trends and advancements.
Browse courses on Vector Databases
Show steps
  • Identify local or online meetups or conferences related to vector databases.
  • Attend sessions, engage in discussions, and network with experts.
Explore Vector Database Tutorials and Examples
Gain practical experience with vector databases by following guided tutorials and examples, reinforcing concepts covered in the course.
Browse courses on Vector Databases
Show steps
  • Find online tutorials on using vector databases like FAISS or Milvus.
  • Follow along and build projects to apply vector database techniques.
Create a presentation on vector databases
Demonstrate your understanding by creating a presentation that summarizes the key concepts of vector databases.
Browse courses on Vector Databases
Show steps
  • Gather information
  • Organize your content
  • Design your slides
  • Practice your presentation
Practice Vector Embedding and Search
Develop proficiency in forming embeddings and searching for similar vectors, honing your skills in working with vector databases.
Show steps
  • Use online platforms or resources (e.g., Hugging Face) for pre-trained embeddings.
  • Experiment with different embedding techniques (e.g., Word2Vec, GloVe).
  • Practice using vector search algorithms (e.g., LSH, Locality Sensitive Hashing).
Contribute to a vector database project
Contribute to a vector database project to enhance your practical skills and gain real-world experience.
Browse courses on Vector Databases
Show steps
  • Find an open-source vector database project
  • Review the project's documentation
  • Identify an area to contribute
  • Submit your contribution
Build a Vector Database Application
Apply your knowledge to build a vector database-based application, deepening your understanding of real-world use cases.
Browse courses on Image Recognition
Show steps
  • Choose a specific application area (e.g., image search, personalized recommendations).
  • Design and implement the vector database schema and application logic.
  • Test and refine your application, evaluating its performance and accuracy.

Career center

Learners who complete Vector Databases: from Embeddings to Applications will develop knowledge and skills that may be useful to these careers:

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Course image
Effort
1 hour
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructor
Sebastian Witalec
Session
August 26, 2024 - September 9, 2024
Language
English
Good to know
Explores the rapidly growing field of vector databases and their applications
Taught by leading expert Sebastian Witalec
Provides a comprehensive overview of vector database concepts and techniques
Covers advanced techniques such as Retrieval Augmented Generation (RAG)
Suitable for intermediate to advanced learners in data science or machine learning
Requires prior knowledge of natural language processing and machine learning
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