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).
Adding Vectors Geometrically: Dynamic Illustration
Link: https://www.geogebra.org/m/tsBer5An
From playlist Trigonometry: Dynamic Interactives!
3 Squares Problem: Trigonometric Identity (Proof Without Words)
Link: https://www.geogebra.org/m/w8r7rn9Q
From playlist Trigonometry: Dynamic Interactives!
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
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!
Writing Equivalent Polar Coordinates Quiz
Link: https://www.geogebra.org/m/MxAvq5Yt
From playlist Trigonometry: Dynamic Interactives!
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
Projection of One Vector onto Another Vector
Link: https://www.geogebra.org/m/wjG2RjjZ
From playlist Trigonometry: Dynamic Interactives!
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)
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!
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
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
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
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]
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
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
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
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)
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
Graphing Trigonometric Functions: Formative Assessment with Feedback
Link: https://www.geogebra.org/m/CSxw82zH BGM: Andy Meyers
From playlist Trigonometry: Dynamic Interactives!