// Deep Learning for Search
Vector-Based Search

Deep Learning can be used to produce a vector representation of both the query and the documents in a corpus of information. A Dense vector describes information as an array of elements, each of them explicitly defined. Various Deep Learning models such as BERT are able to encode textual information as dense vectors, to be used for Dense Retrieval strategies.
This training explores approaches in sparse and dense vector retrieval, including approximate strategies that bring great performance benefits.


• Basic understanding of Search Engines and Machine Learning


• How vector based search works
• How Deep Learning can be used along with Vector Based Search


• Technical Managers
• Technical Team Leaders
• Data scientists
• Software Engineers
• Developers

Based on experience with leading companies including

Watch the intro video

// schedule


If you missed the training, you can easily ask for a private one by contacting us on the button below.

Public Training

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  • Live on Zoom
  • Top expert trainers
  • Certificate of Attendance


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  • Top expert trainers
  • Q&A by e-mail
  • Certificate of Attendance


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If you are looking for intensive sessions tailored on your (or your team) experience, then private trainings are your perfect choice! You can choose between online or live trainings.
  • In-presence or Online
  • Tailored training
  • Top expert trainers
  • Certificate of Attendance
Contact us
// schedule


22nd September 2022

from 03:00 to 07:00 GMT + 1
If you can't participate, you can easily ask for private training by contacting us on the button below.
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700 £

500 £

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Deadline: 2nd September 2022 | 11:59 PM (GMT+1)

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// Deep Learning for Search Vector-Based Search

Our Trainer

Alessandro Benedetti


Alessandro has been involved in designing and developing search-relevant solutions from 2010.
Over the years he has worked on various projects, with various open source technologies aiming to build search solutions able to satisfy the user information needs, often integrating such solutions with machine learning and artificial intelligence technologies.

Andrea Gazzarrini


RRE Creator Andrea Gazzarini is a curious software engineer, mainly focused on the Java language and Search technologies. With more than 15 years of experience in various software engineering areas, his adventure in the search world began in 2010, when he met Apache Solr and later Elasticsearch.    

Full Program

  • Similarity search of dense vectors;
  • Nearest Neighbour Retrieval;
  • Brute-Force Exact Nearest Neighbour;
  • Approximate Nearest Neighbour;
  • Hands on Exercises: Perform ANN search.
Frequently Asked Question

The Deep Learning for Search Vector-Based Search trainings will take place live on zoom!
We are working on being able to provide a recording of the training for those interested.

Everyone can participate on this training, the only prerequisites are written just over there!

Deep Learning for Search Vector-Based Search training last 4 hours.

You will be able to ask every question you have during the training!

Your teacher will be:
  – Alessandro Benedetti, Apache Lucene/Solr committer and Apache Solr PMC member.

Sure, at the end of the training you will receive a certificate of attendance by e-mail.

If you can’t attend last minute you can contact us and reschedule on a different date with our team. Bear in mind this will have to be a private training.  

Yes, you can contact our Sales responsible Oriol Serra and find the best option for you and your team!

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