Showing results for technology Vector Product Vector
GitHub Repo
https://github.com/JPSoteloSilva/amazon-copilot
JPSoteloSilva/amazon-copilot
An AI-powered conversational shopping assistant that uses advanced chatbot technology and vector-based semantic search to help users discover Amazon products through natural language conversations and intelligent recommendations.
GitHub Repo
https://github.com/Helsinki1/emerging-tech
Helsinki1/emerging-tech
Query emerging fields of technology w YC / WellFound / Product-Hunt webscraping and semantic search over a vector database
GitHub Repo
https://github.com/PRACHII03/Coffee---Shop
PRACHII03/Coffee---Shop
Technology Stack utilize a Modern Frontend HTML(Build a systematic SEO friendly Structure)CSS(Including FLEXBOX ),JAVASCRIPT Hand brain of Website & Third Party Libraries )FONTAWSOME for vector icobs & Swiper JS for Touch Response.Functionality & working : Dynamic Product Slider , Navigation ,the header is Sticky & smooth scroll loop based slider.
GitHub Repo
https://github.com/Sadra-Madayeni/Computer-Aided-Design-Course-Projects
Sadra-Madayeni/Computer-Aided-Design-Course-Projects
Comprehensive Digital System Design projects implemented in Verilog. Features include a Vector Dot Product engine and a manually optimized Structural Hash Generator. Demonstrates the complete flow from RTL modeling to logic synthesis and technology mapping, with a comparative analysis of manual vs. automated optimization (Yosys)
GitHub Repo
https://github.com/mohammed558/AzureShop---AI-E-Commerce-Website-
mohammed558/AzureShop---AI-E-Commerce-Website-
An intelligent shopping platform powered by AI that delivers smart product search, recommendations, and chat-based assistance. It uses advanced technologies like semantic search, vector embeddings, and image recognition to enhance user experience.
GitHub Repo
https://github.com/Elamathi27/Flipkart-Sentimental-Review-Analysis
Elamathi27/Flipkart-Sentimental-Review-Analysis
Sentiment analysis on Flipkart reviews using Python, NLP, and machine learning. Includes EDA, data cleaning, TF-IDF vectorization, Naive Bayes classification, and visual insights. Helps improve product strategies and customer satisfaction.🛠Technologies Python, Pandas, NumPy, Seaborn, Matplotlib, Scikit-learn, TextBlob, NLTK, WordCloud
GitHub Repo
https://github.com/ashuthoshc/RightFood_Healthylife
ashuthoshc/RightFood_Healthylife
In the era where order food to calling a friend who on the otherside of the world is just a button way, technology is at its peak. I believe it is making the world smaller, better and faster. Technology like Artificial Intelligence and Machine learning is growing a rapid phase but it seems that there is more than 80% unexplored data which can be utilized for the benefits of mankind. This project is focused on using Machine Learning techniques to extract the information from a dataset. I have used standard Machine Learning techniques to analyze the performance of the several algorithms on this learning task. In addition, I have made use of several libraries like seaborn, scikit learn, pandans etc to understand and visualize my analysis. The dataset used here for analysis contains 40 major food nutrients like calories, fat, vitamins, minerals etc. And it also contains 25 different categories of food products like Beef Products, Vegetable Products, Baked Products etc. Various analysis tools and techniques have used like splitting the dataset, correlation matrixs to understand the dependency of each category of items, Normalization to improve my results, Support Vector Machines and finally prediction to check my learnings. My analysis is approximately 52% accurate. This can be further improved if we are considering other aspects of nutrient chart. This model can be used to predict which type of FoodGroup you are looking at given the nutrient values.
GitHub Repo
https://github.com/sebaaliceio01/ProductRecomendationService
sebaaliceio01/ProductRecomendationService
This repository is an API built with the NestJS framework using TypeScript. The API follows Hexagonal Architecture and Clean Code principles. The main technologies used include NestJS, Pinecone (a vector database), and OpenAI (for creating embeddings).
GitHub Repo
https://github.com/DanielMombeyni/hybrid-search
DanielMombeyni/hybrid-search
This repository implements a hybrid search system that leverages the power of both vector-based and textual search techniques to provide efficient and accurate search results. The system integrates multiple technologies including Pinecone, MeiliSearch, CLIP, and PyTorch to offer a comprehensive solution for indexing and querying product data.
GitHub Repo
https://github.com/sanikabhangaonkar/A-Vector-Space-and-Multi-Criteria-Decision-Modeling-Framework-