Similarity search is the most general term used for a range of mechanisms which share the principle of searching (typically, very large) spaces of objects where the only available comparator is the similarity between any pair of objects. This is becoming increasingly important in an age of large information repositories where the objects contained do not possess any natural order, for example large collections of images, sounds and other sophisticated digital objects. Nearest neighbor search and range queries are important subclasses of similarity search, and a number of solutions exist. Research in Similarity Search is dominated by the inherent problems of searching over complex objects. Such objects cause most known techniques to lose traction over large collections, due to a manifestation of the so-called Curse of dimensionality, and there are still many unsolved problems. Unfortunately, in many cases where similarity search is necessary, the objects are inherently complex. The most general approach to similarity search relies upon the mathematical notion of metric space, which allows the construction of efficient index structures in order to achieve scalability in the search domain. Similarity search evolved independently in a number different scientific and computing contexts, according to various needs. In 2008 a few leading researchers in the field felt strongly that the subject should be a research topic in its own right, to allow focus on the general issues applicable across the many diverse domains of its use. This resulted in the formation of the SISAP foundation, whose main activity is a series of annual international conferences on the generic topic. (Wikipedia).
3 Vector-based Methods for Similarity Search (TF-IDF, BM25, SBERT)
Vector similarity search is one of the fastest-growing domains in AI and machine learning. At its core, it is the process of matching relevant pieces of information together. Similarity search is a complex topic and there are countless techniques for building effective search engines. In
From playlist Vector Similarity Search and Faiss Course
Choosing Indexes for Similarity Search (Faiss in Python)
Facebook AI Similarity Search (Faiss) is a game-changer in the world of search. It allows us to efficiently search a huge range of media, from GIFs to articles - with incredible accuracy in sub-second timescales for billion+ size datasets. The success in Faiss is due to many reasons. One
From playlist Vector Similarity Search and Faiss Course
3 Traditional Methods for Similarity Search (Jaccard, w-shingling, Levenshtein)
Similarity search is one of the fastest-growing domains in AI and machine learning. At its core, it is the process of matching relevant pieces of information together. Similarity search is a complex topic and there are countless techniques for building effective search engines. In this v
From playlist Vector Similarity Search and Faiss Course
This video introduces similarity and explains how to determine if two figures are similar or not. http://mathispower4u.com
From playlist Number Sense - Decimals, Percents, and Ratios
👉 Learn how to solve with similar triangles. Two triangles are said to be similar if the corresponding angles are congruent (equal). Note that two triangles are similar does not imply that the length of the sides are equal but the sides are proportional. Knowledge of the length of the side
From playlist Similar Triangles
Using Similarity and proportions to find the missing values
👉 Learn how to solve with similar triangles. Two triangles are said to be similar if the corresponding angles are congruent (equal). Note that two triangles are similar does not imply that the length of the sides are equal but the sides are proportional. Knowledge of the length of the side
From playlist Similar Triangles
Finding the missing value using similarity in triangles
👉 Learn how to solve with similar triangles. Two triangles are said to be similar if the corresponding angles are congruent (equal). Note that two triangles are similar does not imply that the length of the sides are equal but the sides are proportional. Knowledge of the length of the side
From playlist Similar Triangles
What is the similarity of triangles for SSS
👉 Learn how to solve with similar triangles. Two triangles are said to be similar if the corresponding angles are congruent (equal). Note that two triangles are similar does not imply that the length of the sides are equal but the sides are proportional. Knowledge of the length of the side
From playlist Similar Triangles
Thought Vectors, Knowledge Graphs, and Curious Death(?) of Keyword Search
The world of information retrieval is changing. BERT, Elmo, and the Sesame Street gang are moving in, shouting the gospel of "thought vectors" as a replacement for traditional keyword search. Meanwhile many search teams are now automatically extracting graph representations of the world, t
From playlist Machine Learning
nformation Service Engineering 2021 Prof. Dr. Harald Sack Karlsruhe Institute of Technology Summer semester 2021 Lecture 14: ISE Applications - 2 5.6 Semantic Search - Information Retrieval Dilemma - Semantic Technology Enhanced IR - Semantic Search - Entity-Based Search - Question Answe
From playlist ISE 2021 - Lecture 14, 21.07.2021
Faiss - Introduction to Similarity Search
Full Similarity Search Playlist: https://www.youtube.com/watch?v=AY62z7HrghY&list=PLIUOU7oqGTLhlWpTz4NnuT3FekouIVlqc&index=1 Facebook AI Similarity Search (FAISS) is one of the most popular implementations of efficient similarity search, but what is it - and how can we use it? What is it
From playlist Vector Similarity Search and Faiss Course
nformation Service Engineering 2021 Prof. Dr. Harald Sack Karlsruhe Institute of Technology Summer semester 2021 Lecture 14: ISE Applications - 2 5.7 Exploratory Search - Beyond Search - Retrieval vs Exploration - Exploratory Search - Linked Data Based Exploratory Search - Exploration an
From playlist ISE 2021 - Lecture 14, 21.07.2021
Spotify's Podcast Search Explained
The market for podcasts has grown tremendously in recent years. Driving the charge in podcast adoption is Spotify. In a few short years, they have become the undisputed leaders in podcasting. Despite only entering the game in 2018, by late 2021, Spotify had already usurped Apple, the long
From playlist Recommended
Beyond Semantic Search with OpenAI and Pinecone
An interactive and practical workshop on building AI-powered semantic applications on top of OpenAI and Pinecone. We will do an end-to-end tutorial on building semantic search, text summarization, question-answering, entity extraction, and other applications. No ML expertise is needed: If
From playlist Talks
Boost Your Search with Apache Solr | Solr Search Engine Tutorial | Edureka
( Apache Solr Certification Training - https://www.edureka.co/apache-solr-self-paced ) Watch Sample Recording : http://www.edureka.co/apache-solr?utm_source=youtube&utm_medium=referral&utm_campaign=boost-search-solr Apache Solr based on the Lucene Library, is an open-source enterprise Gr
From playlist Apache Solr Tutorial Videos
Large language Models for Real-world Applications | Future of Data & AI | Data Science Dojo
Machine language understanding and generation have undergone rapid improvements due to recent breakthroughs in machine learning (e.g. large language models like GPT and BERT). And while big tech and NLP engineers were quick to capitalize on these models, the broader developer community lag
From playlist Future of Data and AI: Tutorials
Keyword Research: How To Target The “Right” Keyword [3.3]
In this video, you’ll learn how to find the best keyword to target for your content. *************************************** Additional Blogging Resources Blogging For Business Course (Complete Playlist) ► https://www.youtube.com/watch?v=y5kQXogrLN0&list=PLvJ_dXFSpd2tbI3mYhAO8oZ2NYMrJuN
From playlist Blogging for Business Course
Learn Sentence Transformers #SBERT: Update 2022 - new models, semantic search, AI #colab (SBERT 23)
Update on latest Sentence Transformers (SBERT) pretrained models for semantic search. Symmetric & asymmetric semantic search, Pytorch on Colab. Depending on a semantic search of a 768 dimensional sentence embedding in a topological space four pretrained SBERT models are recommended and th
From playlist SBERT: Python Code Sentence Transformers: a Bi-Encoder /Transformer model #sbert
Given two similar triangles determine the values of x and y for the angles
👉 Learn how to solve with similar triangles. Two triangles are said to be similar if the corresponding angles are congruent (equal). Note that two triangles are similar does not imply that the length of the sides are equal but the sides are proportional. Knowledge of the length of the side
From playlist Similar Triangles