String matching algorithms

Trigram search

Trigram search is a method of searching for text when the exact syntax or spelling of the target object is not precisely known or when queries may be regular expressions. It finds objects which match the maximum number of three consecutive character strings (i.e. trigrams) in the entered search terms, which are generally near matches. Two strings with many shared trigrams can be expected to be very similar. Trigrams also allow for efficiently creating indexes for searches that are regular expressions or match the text inexactly. Indexes can significantly accelerate searches. A threshold for number of trigram matches can be specified as a cutoff point, after which a result is no longer considered a match. Using trigrams for accelerating searches is a technique used in some systems for code searching, in situations in which queries that are regular expressions may be useful, in search engines such as Elasticsearch, as well as in databases such as PostgreSQL. (Wikipedia).

Video thumbnail

Adding Vectors Geometrically: Dynamic Illustration

Link: https://www.geogebra.org/m/tsBer5An

From playlist Trigonometry: Dynamic Interactives!

Video thumbnail

Using trig identities to verify an identity

👉 Learn how to verify trigonometric identities involving the addition and subtraction of terms. To do this it is usually useful to convert the addition or subtraction terms in terms of one trigonometric function and then evaluate. Another very useful method is to convert all terms to the

From playlist Verify Trigonometric Identities

Video thumbnail

Composing Trig & Inverse Trig Functions (2)

Evaluating compositions of #trig & inverse #trig functions: More quick formative assessment via #geogebra: https://www.geogebra.org/m/rwpkkmt7 & https://www.geogebra.org/m/hcw4fr6t #MTBoS #ITeachMath #trigonometry #precalc #math #mathchat

From playlist Trigonometry: Dynamic Interactives!

Video thumbnail

Writing Equivalent Polar Coordinates Quiz

Link: https://www.geogebra.org/m/MxAvq5Yt

From playlist Trigonometry: Dynamic Interactives!

Video thumbnail

Learn how to verify a trig identity

👉 Learn how to verify trigonometric identities involving the addition and subtraction of terms. To do this it is usually useful to convert the addition or subtraction terms in terms of one trigonometric function and then evaluate. Another very useful method is to convert all terms to the

From playlist Verify Trigonometric Identities

Video thumbnail

Projection of One Vector onto Another Vector

Link: https://www.geogebra.org/m/wjG2RjjZ

From playlist Trigonometry: Dynamic Interactives!

Video thumbnail

Compound Data Embeddings: Handling Text + Graph Data by Akshar Varma

DISCUSSION MEETING THE THEORETICAL BASIS OF MACHINE LEARNING (ML) ORGANIZERS: Chiranjib Bhattacharya, Sunita Sarawagi, Ravi Sundaram and SVN Vishwanathan DATE : 27 December 2018 to 29 December 2018 VENUE : Ramanujan Lecture Hall, ICTS, Bangalore ML (Machine Learning) has enjoyed tr

From playlist The Theoretical Basis of Machine Learning 2018 (ML)

Video thumbnail

Evaluating Trigonometric Functions of Angles Given a Point on its Terminal Ray

Math Ts: SAVE TIME & have your Trigonometry Ss (formatively) assess their own work! After solving a problem or 2 (like this), send them here: https://www.geogebra.org/m/hK5QfXah .

From playlist Trigonometry: Dynamic Interactives!

Video thumbnail

Decoupling Applications from Architectures - Jeff Hoffer - JSConf US 2019

Software is the most malleable building material we've ever created, and yet Technical Debt continues to plague the choices we make when building applications. When we talk about starting new projects, there's always a debate over getting something out the door knowing we're taking on Tec

From playlist JSConf US 2019

Video thumbnail

6.1: Intro to Session 6: Markov Chains - Programming with Text

This video introduces Session 6: Markov Chains (http://shiffman.net/a2z/markov). It is part of the ITP course "Programming from A to Z". A Markov Chain is a broad concept, in this series I will demonstrate it as a means to generate text algorithmically, using n-grams and probability. Cou

From playlist Programming with Text - All Videos

Video thumbnail

Alan M. Turing Centennial Conference: Turing's Estimation Technique and Large-scale Machine Learning

Turing's Estimation Technique and Large-scale Machine Learning Presented by Prof. Corinna Cortes, Google Alan M. Turing Centennial Conference - Israel April 4, 2012 The Wohl Centre Bar-Ilan University Ramat-Gan, Israel For more information see: https://sites.google.com/site/turingcentena

From playlist Alan M. Turing Centennial Conference - Israel

Video thumbnail

Lecture 4/16 : Learning feature vectors for words

Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] 4A Learning to predict the next word 4B A brief diversion into cognitive science 4C Another diversion : The softmax output function 4D Neuro-probabilistic language models 4E Ways to deal with the large number of possi

From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]

Video thumbnail

Learn how to verify the identity

👉 Learn how to verify trigonometric identities involving the addition and subtraction of terms. To do this it is usually useful to convert the addition or subtraction terms in terms of one trigonometric function and then evaluate. Another very useful method is to convert all terms to the

From playlist Verify Trigonometric Identities

Video thumbnail

Introduction to Natural Language Processing | NLP Tutorial | Edureka | ML/DS Live - 1

🔥Edureka's Post Graduate Program in AI & Machine Learning with NIT Warangal: https://www.edureka.co/post-graduate/machine-learning-and-ai This Edureka video will provide you with a detailed description of NLP (Natural Language Processing). You will also learn about the various applications

From playlist Brief Introduction to Data Science

Video thumbnail

Learn SBERT Sentence Embedding: SBERT TSDAE - Transformer based Denoising AutoEncoder (SBERT 22)

SBERT TSDAE (Transformer based Denoising Auto Encoder): You want to code Sentence Transformers (based on BERT models) to extract semantic information on millions of documents? Here is your python code - with October 2021 updates. New pre-trained models of BERT transformer models and Sente

From playlist SBERT: Python Code Sentence Transformers: a Bi-Encoder /Transformer model #sbert

Video thumbnail

How to Create Bigrams and Trigrams and Remove Frequent Words (Topic Modeling for DH 03.04)

LDA Bigram and Trigram source: https://www.machinelearningplus.com/nlp/topic-modeling-gensim-python/#9createbigramandtrigrammodels Sources for TF-IDF: https://stackoverflow.com/questions/24688116/how-to-filter-out-words-with-low-tf-idf-in-a-corpus-with-gensim/35951190 If you enjoy this v

From playlist Topic Modeling and Text Classification with Python for Digital Humanities (DH)

Video thumbnail

Coding Challenge #42.1: Markov Chains - Part 1

In Part 1 of this Coding Challenge, I discuss the concepts of "N-grams" and "Markov Chains" as they relate to text. I use Markova chains to generate text automatically based on a source text. 💻Challenge Webpage: https://thecodingtrain.com/CodingChallenges/042.1-markov-chains.html 💻Program

From playlist Programming with Text - All Videos

Video thumbnail

Graphing Trigonometric Functions: Formative Assessment with Feedback

Link: https://www.geogebra.org/m/CSxw82zH BGM: Andy Meyers

From playlist Trigonometry: Dynamic Interactives!

Related pages

Search engine indexing | Regular expression | Approximate string matching | Search algorithm | N-gram | String (computer science)