Showing results for require Vector Vector Vector Product
GitHub Repo
https://github.com/tahsinkoc/embrix
tahsinkoc/embrix
A lightweight, production-ready NPM package for generating text embeddings locally using @xenova/transformers. Supports MiniLM and BGE models with 384-dimensional vectors. Features include batch processing, similarity functions (cosine, euclidean, dot product), and built-in benchmarking tools. No external API calls required - runs entirely in Node.
GitHub Repo
https://github.com/datagirl98/Required-Assignment-17.1
datagirl98/Required-Assignment-17.1
In this third practical application assignment, your goal is to compare the performance of the classifiers (k-nearest neighbors, logistic regression, decision trees, and support vector machines) you encountered in this section of the program. You will use a dataset related to the marketing of bank products over the telephone.
GitHub Repo
https://github.com/frknrnn/Covid19_classification_tmempr
frknrnn/Covid19_classification_tmempr
Medical images are crucial data sources for not easily diagnosed diseases. X-rays, one of the medical images, have high resolution. Processing high-resolution images leads to a few problems such as the difficulties in data storage, the computational load, and the time required to process high-dimensional data. It is a vital element to be able to diagnose diseases fast and accurately. In this study, a data set consisting of lung X-rays of patients with and without COVID-19 symptoms was taken into consideration and disease diagnosis from these images can be summarized in 2 steps as preprocessing and classification. Preprocessing step is the feature extraction process and in this step, the recently developed decomposition-based method Tridiagonal Matrix Enhanced Multivariance Products Representation (TMEMPR) is proposed as a feature extraction method. Classification of images is the second step where the Random Forest and Support Vector Machine (SVM) is applied as classifiers. Also, X-ray images have been reduced by 99,9\% with TMEMPR and with several state-of-the-art feature extraction methods which are Discrete Wavelet Transform (DWT), Discrete Cosine Transform(DCT) The results are examined under different feature extraction methods. It is observed that a higher accuracy rate of classification is achieved by using the TMEMPR method.
GitHub Repo
https://github.com/arpit3043/Extractive-Text-Summerization
arpit3043/Extractive-Text-Summerization
Summarization systems often have additional evidence they can utilize in order to specify the most important topics of document(s). For example, when summarizing blogs, there are discussions or comments coming after the blog post that are good sources of information to determine which parts of the blog are critical and interesting. In scientific paper summarization, there is a considerable amount of information such as cited papers and conference information which can be leveraged to identify important sentences in the original paper. How text summarization works In general there are two types of summarization, abstractive and extractive summarization. Abstractive Summarization: Abstractive methods select words based on semantic understanding, even those words did not appear in the source documents. It aims at producing important material in a new way. They interpret and examine the text using advanced natural language techniques in order to generate a new shorter text that conveys the most critical information from the original text. It can be correlated to the way human reads a text article or blog post and then summarizes in their own word. Input document → understand context → semantics → create own summary. 2. Extractive Summarization: Extractive methods attempt to summarize articles by selecting a subset of words that retain the most important points. This approach weights the important part of sentences and uses the same to form the summary. Different algorithm and techniques are used to define weights for the sentences and further rank them based on importance and similarity among each other. Input document → sentences similarity → weight sentences → select sentences with higher rank. The limited study is available for abstractive summarization as it requires a deeper understanding of the text as compared to the extractive approach. Purely extractive summaries often times give better results compared to automatic abstractive summaries. This is because of the fact that abstractive summarization methods cope with problems such as semantic representation, inference and natural language generation which is relatively harder than data-driven approaches such as sentence extraction. There are many techniques available to generate extractive summarization. To keep it simple, I will be using an unsupervised learning approach to find the sentences similarity and rank them. One benefit of this will be, you don’t need to train and build a model prior start using it for your project. It’s good to understand Cosine similarity to make the best use of code you are going to see. Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. Since we will be representing our sentences as the bunch of vectors, we can use it to find the similarity among sentences. Its measures cosine of the angle between vectors. Angle will be 0 if sentences are similar. All good till now..? Hope so :) Next, Below is our code flow to generate summarize text:- Input article → split into sentences → remove stop words → build a similarity matrix → generate rank based on matrix → pick top N sentences for summary.
GitHub Repo
https://github.com/grantbilker/PaintByNumbers
grantbilker/PaintByNumbers
This project is meant to take any image (preferably higher contrast) and turn it into a semi-vectorized template for use as a paint by numbers template. The products from this should include: the template image in the specified dimensions, the template's color palette (the number of colors specified), potentially a configuration panel for absolute color choices (GUI), potentially an ability to vectorize as opposed to reimaging, potentially the ratios of certain paints required to mix each of the colors in the palette
GitHub Repo
https://github.com/teamchong/turboquant-wasm
teamchong/turboquant-wasm
TurboQuant WASM SIMD vector compression — 3 bits/dim with fast dot product. Requires relaxed SIMD (Chrome 114+, Firefox 128+, Safari 18+, Node 20+)
GitHub Repo
https://github.com/amirmohammadnajafi/classification-support-vector-machine
amirmohammadnajafi/classification-support-vector-machine
The data is related to direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be subscribed ('yes') or not ('no') subscribed.
GitHub Repo
https://github.com/binoydutt/Resume-Job-Description-Matching
binoydutt/Resume-Job-Description-Matching
The purpose of this project was to defeat the current Application Tracking System used by most of the organization to filter out resumes. In order to achieve this goal I had to come up with a universal score which can help the applicant understand the current status of the match. The following steps were undertaken for this project 1) Job Descriptions were collected from Glass Door Web Site using Selenium as other scrappers failed 2) PDF resume parsing using PDF Miner 3) Creating a vector representation of each Job Description - Used word2Vec to create the vector in 300-dimensional vector space with each document represented as a list of word vectors 4) Given each word its required weights to counter few Job Description specific words to be dealt with - Used TFIDF score to get the word weights. 5) Important skill related words were given higher weights and overall mean of each Job description was obtained using the product for word vector and its TFIDF scores 6) Cosine Similarity was used get the similarities of the Job Description and the Resume 7) Various Natural Language Processing Techniques were identified to suggest on the improvements in the resume that could help increase the match score
GitHub Repo
https://github.com/bushra-ansari/Predicting-Term-Deposit-Subscription-by-a-Client-by-SVM-Classifier
bushra-ansari/Predicting-Term-Deposit-Subscription-by-a-Client-by-SVM-Classifier
Support Vector Machine Classification model is applied on bank dataset containing 41188 rows and 21 columns. The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to assess if the product (bank term deposit) would be ('yes') or not ('no') subscribed.
GitHub Repo
https://github.com/kamelmohammedmohammed/Twigs-Classifier