Modern search engines explained. What you need to know about today’s vector search engines, explained on a high-level with visuals!
�� Check out Weaviate: Our sponsor’s open-source customizable search engine and vector database.
► Get started with Weaviate: https://www.semi.technology/developers/weaviate/current/
► Weaviate on GitHub: https://github.com/semi-technologies/weaviate
► �� Learn more about Weaviate: https://youtu.be/IExopg1r4fw

⏭️ Ms. Coffee Bean’s Multimodalities: https://youtube.com/playlist?list=PLpZBeKTZRGPNKxoNaeMD9GViU_aH_HJab

Outline:
00:00 Intro to search engines and our sponsor
01:12 Naive search engines
03:35 AI-power: Vector search engines explained
06:05 Vectors and why we need them
06:49 How to vectorizer models work?
10:38 Adapting the ML-model to your use case
12:20 Summary & playing with Weaviate

Thanks to our Patrons who support us in Tier 2, 3, 4: ��
donor, Dres. Trost GbR

▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀
�� Optionally, pay us a coffee to help with our Coffee Bean production! ☕
Patreon: https://www.patreon.com/AICoffeeBreak
Ko-fi: https://ko-fi.com/aicoffeebreak
▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀

�� Links:
AICoffeeBreakQuiz: https://www.youtube.com/c/AICoffeeBreak/community
Twitter: https://twitter.com/AICoffeeBreak
Reddit: https://www.reddit.com/r/AICoffeeBreak/
YouTube: https://www.youtube.com/AICoffeeBreak

#AICoffeeBreak #MsCoffeeBean #Weaviate #SeMI #MachineLearning #AI

Let’s reach 100K subscribers ���� https://www.youtube.com/c/AhmadBazzi?sub_confirmation=1

��About
The SVM method is a popular method to solve the classification problem in machine learning. It finds applications in many fields, such as Artificial Intelligence, Signal Processing, Pattern Recognition, data mining, and so on. The lecture is outlined as follows:

⏲Outline⏲
00:00 Intro
00:52 Classification in Machine Learning
04:29 The Support-Vector Machine method
08:31 SVM’s decision rule: Mathematical Formulation
13:06 SVM’s derivation: The convex optimization approach
24:59 SVM MATLAB implementation
35:52 Outro

�� Subscribe for more videos on Machine Learning Applications
�� Smash that like button, in case you find this tutorial useful.
��‍�� Speak up and comment, I am all ears.

Similarity between elements in a dataset has traditionally been measured based on appearance – simple measures such as word counts and other lexical similarities have been the state of the practice. Vector Search goes beyond appearances and lets you define similarity based on meanings and deeper representations of content. Image recognition and comparisons, audio comparisons and recommendations, and relevance ranking based on Natural Language Processing (NLP) are just a few of the applications that Vector Search enables. The Elastic Platform equips you with the tools you need to create novel applications based on this approach.

Highlights
– Understand the basics of Vector Search
– Define indexes to hold vectorized data using Elastic’s dense_vector field type
– Perform efficient Approximate Nearest Neighbor search of vectorized data data using the Hierarchical Navigable Small World (HNSW) search algorithm
– Understand how to import machine learning models into Elasticsearch and use them for inference
Agenda

Introduction and Overview of Vector Search
Use Cases
Measuring Vector Similarity
Vector Search at Scale
Getting Started Hands On
Additional Information
Q &A

Presenter: Robert Statsinger – Principal Solution Architect @ Elastic

2-Minute crash course on Support Vector Machine, one of the simplest and most elegant classification methods in Machine Learning. Unlike neural networks, SVMs can work with very small datasets and are not prone to overfitting.

————————–

This video would not have been possible without the help of Gökçe Dayanıklı.