Showing results for process Vector Vector Product Vector Vector Vector Vector Product
GitHub Repo
https://github.com/UtkarshPanara/Predicting-product-quality-for-Tennesee-Eastman-process
UtkarshPanara/Predicting-product-quality-for-Tennesee-Eastman-process
Prediction of product quality using Deep Neural Network and Support Vector Regression
GitHub Repo
https://github.com/unais5/Vector-Space-Model
unais5/Vector-Space-Model
The query processing of VSM is quite tricky, you need of optimize every aspect of computation. The high-dimensional vector product and similarity values of query (q) and documents (d) need to optimized. Basic Assumption for Vector Space Model (VSM) Retrieval Model 1.Simple model based on linear algebra. Terms are considered as features using a weighting scheme. 2.Allows partial matching of documents with the queries. Hence, able to produce good institutive scoring. Continuous scoring between queries and documents. 3.Ranking of documents are possible using relevance score between document and query.
GitHub Repo
https://github.com/zer0-A1/Semantic_Search
zer0-A1/Semantic_Search
A FastAPI semantic search system for company/product data. It processes Excel/CSV files, uses OpenAI's text-embedding-3-small model to generate AI embeddings, and performs vector similarity searches via PostgreSQL with pgvector.
GitHub Repo
https://github.com/Amagnum/Parallel-Dot-Product-of-2-vectors-MPI
Amagnum/Parallel-Dot-Product-of-2-vectors-MPI
Computing the vector-vector multiplication on p processors using block-striped partitioning for uniform data distribution. Assuming that the vectors are of size n and p is the number of processors used and n is a multiple of p.
GitHub Repo
https://github.com/DheerajKumar97/Amazon-Product-Reviews-LSTM--NLP-with-Deployment
DheerajKumar97/Amazon-Product-Reviews-LSTM--NLP-with-Deployment
This project is designed to predict Amazon Product review sentiment analysis and this analysis is used to give feedback about the quality of the Products. This project is implemented using Natural Language processing using a bag of words model and other techniques like vectorization to analyze the product reviews.
GitHub Repo
https://github.com/Mizanur888/VectorDotProductWith_MPI_Scatter
Mizanur888/VectorDotProductWith_MPI_Scatter
In this assignment we parallelize the matrix-vector product to calculate dot product more efficiently. To reducing the calculation time, we send chunks to array to different processes, so that each process can calculate the dot product of chunks of array. Also, we calculate the start, end and total time to determine how long it taken by each process.
GitHub Repo
https://github.com/MurtazaAbidi/Vector-Space-Model
MurtazaAbidi/Vector-Space-Model
The query processing of VSM is quite tricky, you need of optimize every aspect of computation. The high-dimensional vector product and similarity values of query (q) and documents (d) need to optimized.
GitHub Repo
https://github.com/Alamart1683/mpi-vectors-scalar-product-for-n-process
Alamart1683/mpi-vectors-scalar-product-for-n-process
Вычисление скалярного произведения векторов с помощью MPI
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/priyanka387/LangChain-Vector-Databases-in-Production