In data analysis, cosine similarity is a measure of similarity between two sequences of numbers. For defining it, the sequences are viewed as vectors in an inner product space, and the cosine similarity is defined as the cosine of the angle between them, that is, the dot product of the vectors divided by the product of their lengths. It follows that the cosine similarity does not depend on the magnitudes of the vectors, but only on their angle. The cosine similarity always belongs to the interval For example, two proportional vectors have a cosine similarity of 1, two orthogonal vectors have a similarity of 0, and two opposite vectors have a similarity of -1. The cosine similarity is particularly used in positive space, where the outcome is neatly bounded in . For example, in information retrieval and text mining, each word is assigned a different coordinate and a document is represented by the vector of the numbers of occurrences of each word in the document. Cosine similarity then gives a useful measure of how similar two documents are likely to be, in terms of their subject matter, and independently of the length of the documents. The technique is also used to measure cohesion within clusters in the field of data mining. One advantage of cosine similarity is its low complexity, especially for sparse vectors: only the non-zero coordinates need to be considered. Other names for cosine similarity include Orchini similarity and Tucker coefficient of congruence; the Otsuka–Ochiai similarity (see below) is cosine similarity applied to binary data. (Wikipedia).
Trigonometry 5 The Cosine Relationship
A geometrical explanation of the law of cosines.
From playlist Trigonometry
Cosine Similarity | Introduction to Text Analytics with R Part 10
Cosine Similarity includes specific coverage of: – How cosine similarity is used to measure similarity between documents in vector space. – The mathematics behind cosine similarity. – Using cosine similarity in text analytics feature engineering. – Evaluation of the effectiveness of the c
From playlist Introduction to Text Analytics with R
Cosine Similarity, Clearly Explained!!!
The Cosine Similarity is a useful metric for determining, among other things, how similar or different two text phrases are. I'll be honest, the first time I saw the equation for The Cosine Similarity, I was scared. However, it turns out to be really quite simple, and this StatQuest walks
From playlist StatQuest
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
Applying the law of cosines to solve a word problem
Learn how to solve for the lengths of the sides and the measures of the angles of a triangle using the law of cosines. The law of cosines is used in determining the lengths of the sides or the measures of the angles of a triangle when no angle measure and the length of the side opposite th
From playlist Solve Law of Cosines (Word Problem) #ObliqueTriangles
Similar Triangles Using Side-Side-Side and Side-Angle-Side
This video explains how to determine if two triangles are similar using SSS and SAS. Complete Video List: http://www.mathispower4u.yolasite.com
From playlist Similarity
What Are Covalent Bonds | Properties of Matter | Chemistry | FuseSchool
What Are Covalent Bonds | Properties of Matter | Chemistry | FuseSchool Learn the basics about covalent bonds, when learning about properties of matter. When similar atoms react, like non-metals combining with other non-metals, they share electrons. This is covalent bonding. Non-metals
From playlist CHEMISTRY
Applying the law of cosines when given SAS
Learn how to solve for the lengths of the sides and the measures of the angles of a triangle using the law of cosines. The law of cosines is used in determining the lengths of the sides or the measures of the angles of a triangle when no angle measure and the length of the side opposite th
From playlist Law of Cosines
Training State-of-the-Art Sentence Embedding Models
Detailed talk about how to train state-of-the-art sentence embedding models. The talks does a deep-dive on the Multiple-Negatives-Ranking-Loss: https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss the best method to train sentence embedding models. It c
From playlist Introduction to Dense Text Representation
#SOME2 Deriving the general formula for the cosine of a sum of angles
This video is a submission for the Summer of Math Exposition #2. I hope you enjoyed, and apologies for the late upload.
From playlist Summer of Math Exposition 2 videos
Advanced Semantic SEARCH w/ SBERT (Re-Ranking w/ Cross-Encoder) Edition 2022 #sbert (SBERT 28)
SBERT Sentence Transformers Q&A cosine-similarity and re-ranking w/ Cross-Encoders (BERT) for advanced Semantic Search in PyTorch. BI-Encoder combined w/ Cross-Encoder. Pytorch code, Jupyter, Colab. Advanced Semantic Search w/ SBERT Cross-Encoder. Cross-encoder are pre-trained from Huggi
From playlist Learn SBERT CROSS-Encoder: Sentence Transformers for Semantic Search, Zero-shot, QA and Domain Knowledge Transfer
Using the law of cosines for a triangle with SAS
Learn how to solve for the lengths of the sides and the measures of the angles of a triangle using the law of cosines. The law of cosines is used in determining the lengths of the sides or the measures of the angles of a triangle when no angle measure and the length of the side opposite th
From playlist Law of Cosines
how to measure similarity in vector space (cosine similarity)
introduce Euclidean Distance and Cosine similarity with easy example for easy understanding to NLP (natural language processing) deep learning students. all machine learning youtube videos from me, https://www.youtube.com/playlist?list=PLVNY1HnUlO26x597OgAN8TCgGTiE-38D6
From playlist Machine Learning