Showing results for study Vector Vector Product Product
GitHub Repo
https://github.com/byukan/Marketing-Data-Science
byukan/Marketing-Data-Science
Analytics and data science business case studies to identify opportunities and inform decisions about products and features. Topics include Markov chains, A/B testing, customer segmentation, and machine learning models (logistic regression, support vector machines, and quadratic discriminant analysis).
GitHub Repo
https://github.com/DataQUEEN99/NeuroFlow-AI-Personal-AI-Brain-for-Learning-Productivity
DataQUEEN99/NeuroFlow-AI-Personal-AI-Brain-for-Learning-Productivity
A next-generation full-stack AI Personal Operating System (AI-POS) that acts as your persistent digital brain. NeuroFlow AI remembers your goals, tracks learning, executes tasks autonomously, and continuously optimizes your work, study, and life. Built with Next.js, React, Tailwind CSS, FastAPI, and vector memory for intelligent long-term context.
GitHub Repo
https://github.com/MIJUMBO/Cosine_Similarity_Case_Study
MIJUMBO/Cosine_Similarity_Case_Study
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. Similarity measures have a multitude of uses in machine learning projects; they come in handy when matching strings, measuring distance, and extracting features.
GitHub Repo
https://github.com/UtkuKacar/Time-Series-Analysis-of-Electricity-Production
UtkuKacar/Time-Series-Analysis-of-Electricity-Production
This project aims to analyze seasonal and trend changes in electricity production in India using time series analysis methods. The study utilizes annual electricity production data from India to create and analyze a Support Vector Regression (SVR) model.
GitHub Repo
https://github.com/arkidbera/Fission-product-release
arkidbera/Fission-product-release
Fission product release has been a matter of study since many years. Its importance lies in the fact that the release of radionuclides affect radiological impact due to a postulated accident scenario. The fission product gases released mainly consist of Xenon and Krypton, due to their low boiling point temperature and insolubility in the fuel matrix. These gases are released from fuel pellets due to the rupture of cladding to the coolant in the primary heat transport system. No standard method for calculation of fission product release gases has yet been found, due to the involvement of numerous factors, leading to variance in results. In our project, we have attempted to do the same, by creating a fission release model using various relations and carrying out certain computations on MATLAB, which includes vectorized implementations, iterative computations and plotting various curves to visualize the results obtained. The aim of our model is to find the fractional release of fission product gases with respect to fuel temperature, which depends on linear heat rating (i.e. power extracted per unit length of fuel pin) and burnup of the fuel. Our model also finds certain curves representing the behaviour of fractional release with respect to burnup, by varying grain radius and Diffusion Coefficient. The model has further been used as a tool for certain time varying analyses, where we have simulated a condition where the average temperature of the fuel pellets is changing with time, and thus, found the value of release fraction for those time instants. Finally, certain statistical analyses have been carried out, which include input parameter uncertainty, model parameter uncertainty and time-varying error analysis. We have used algorithms like Box-Muller transform in our study. Also, tolerance bands have been derived using confidence interval values for time-varying error analysis.
GitHub Repo
https://github.com/HarikumaranJ/n8n_Workflow_Product
HarikumaranJ/n8n_Workflow_Product
Personal Case Study - n8n Orchestration, Pinecone Vector DB, Context Window, Embedding, Chucking, Chunk Overlap, Rerank, RAG Retrieval, OpenAPI LLM, Cohere Model
GitHub Repo
https://github.com/MeghanHan/PA3-Comparing-Classifiers
MeghanHan/PA3-Comparing-Classifiers
This study compares the performance of the classifiers (k-nearest neighbors, logistic regression, decision trees, and support vector machines). The dataset is related to the marketing of bank products over the telephone.
GitHub Repo
https://github.com/DataQUEEN99/NeuroFlow-AI-_Personal-AI-Brain-for-Learning-Productivity
DataQUEEN99/NeuroFlow-AI-_Personal-AI-Brain-for-Learning-Productivity
A next-generation full-stack AI Personal Operating System (AI-POS) that acts as your persistent digital brain. NeuroFlow AI remembers your goals, tracks learning, executes tasks autonomously, and continuously optimizes your work, study, and life. Built with Next.js, React, Tailwind CSS, FastAPI, and vector memory for intelligent long-term context.
GitHub Repo
https://github.com/segfaultscribe/SIMD-Dot-product-Optimization
segfaultscribe/SIMD-Dot-product-Optimization
Performance case study of dot product optimizations using SIMD (SSE, AVX, AVX2), analyzing speedups from scalar to vectorized implementations with benchmarking and profiling.
GitHub Repo
https://github.com/somjit101/NLP-CaseStudy-Amazon-Fine-Foods-Review