Showing results for Q Background Vector
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.dropdown-content{display:none} </style> <!-- <link rel="stylesheet" href="/static/css/metrics/metrics.css"> --> <style>#content h3{margin-bottom:16px}.data-panel{max-width:768px}.data-panel .description{margin-bottom:1em}.metric-nav{list-style-type:none}.metric-nav h3:not(:first-of-type){margin-top:32px}.metric-nav li{text-align:center;border-top-left-radius:3px;border-top-right-radius:3px;background:linear-gradient(to bottom, white, #F2F2F2);box-shadow:1px 1px 4px rgba(0,0,0,0.065);padding:0.5em;margin-bottom:10px}@media only screen and (max-width: 700px){#subheader{margin:16px 0;text-align:center}.data-panel{margin:0 10px}} </style> <script> window.Polymer = window.Polymer || { dom: 'shadow', // Use native shadow dom. lazyRegister: 'max', useNativeCSSProperties: true, suppressTemplateNotifications: true, // Don't fire dom-change on dom-if, dom-bind, etc. suppressBindingNotifications: true // disableUpgradeEnabled: true // Works with `disable-upgrade` attr. When removed, upgrades element. }; var $ = function(selector) { return document.querySelector(selector); }; var $$ = function(selector) { return document.querySelectorAll(selector); }; </script> <style is="custom-style"> app-drawer { --app-drawer-width: 200px; --app-drawer-content-container: { background: #eee; }; } paper-item { --paper-item: { cursor: pointer; }; } </style> <link rel="import" href="/static/elements/metrics-imports.vulcanize.html"> </head> <body class="loading"> <!--<div id="site-banner"> <a href="https://www.youtube.com/watch?v=Rd0plknSPYU" target="_blank"> <iron-icon icon="chromestatus:ondemand-video"></iron-icon> How we built it</a> </div>--> <app-drawer-layout fullbleed> <app-drawer swipe-open> <div class="drawer-content-wrapper"> <ul class="metric-nav"> <h3>All properties</h3> <li><a href="/metrics/css/popularity">Stack rank</a></li> <li><a href="/metrics/css/timeline/popularity">Timeline</a></li> <h3>Animated properties</h3> <li><a href="/metrics/css/animated">Stack rank</a></li> <li><a href="/metrics/css/timeline/animated">Timeline</a></li> </ul> </div> </app-drawer> <app-header-layout> <app-header reveals fixed effects="waterfall"> <div class="main-toolbar"> <div class="toolbar-content"> <header> <aside> <iron-icon icon="chromestatus:menu" drawer-toggle></iron-icon> <hgroup> <a href="/features" target="_top"><h1>Chrome Platform Status</h1></a> <span>feature support & usage metrics</span> </hgroup> </aside> <nav> <a href="/features">Features</a> <a href="/samples" class="features">Samples</a> <paper-menu-button vertical-align="top" horizontal-align="right"> <a href="javascript:void(0)" class="dropdown-trigger">Usage Metrics</a> <div class="dropdown-content" hidden> <!-- hidden removed by lazy load code. --> <a href="/metrics/css/popularity" class="metrics">CSS</a> <a href="/metrics/feature/popularity" class="metrics">JS/HTML</a> </div> </paper-menu-button> </nav> </header> <div id="subheader"> <h2>CSS usage metrics > animated properties > timeline</h2> </div> </div> </div> </app-header> <div id="content"> <div id="spinner"><img src="/static/img/ring.svg"></div> <div class="data-panel"> <p class="description">Percentages are the number of times (as the fraction of all animated properties) this property is animated.</p> <chromedash-feature-timeline type="css" view="animated" title="Percentage of times (as the fraction of all animated properties) this property is animated." ></chromedash-feature-timeline> </div> </div> </app-header-layout> <footer> <p>Except as otherwise noted, the content of this page under <a href="https://creativecommons.org/licenses/by/2.5/">CC Attribution 2.5</a> license. 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GitHub Repo
https://github.com/basil4240/document-intelligence-api
basil4240/document-intelligence-api
An async FastAPI service for document Q&A and semantic search. Accepts PDF and DOCX uploads, chunks and embeds content using OpenAI embeddings, stores vectors in PGVector, and exposes a semantic search and RAG-powered Q&A endpoint. Background processing via Celery and Redis.
GitHub Repo
https://github.com/suraj-naithani/PDF-reader
suraj-naithani/PDF-reader
Full-stack PDF Reader with Q&A support. Upload PDFs, process with LangChain + OpenAI embeddings, and query content via natural language. Features chunking, Qdrant vector storage, BullMQ background jobs, and a Next.js + Tailwind frontend with Clerk auth.
GitHub Repo
https://github.com/WillowsCosmic/ConversePDF
WillowsCosmic/ConversePDF
ConversePDF is an AI-powered document Q&A app that lets users upload PDFs and ask questions about them. It uses FastAPI for the backend, Streamlit for the UI, LlamaIndex to process documents and orchestrate RAG, Qdrant as a vector database to store document embeddings, and Inngest to handle background processing jobs asynchronously
GitHub Repo
https://github.com/anayaz/quantum_svm
anayaz/quantum_svm
Quantum Support Vector Machine for signal-background classification.
GitHub Repo
https://github.com/tamaraceranic/Support-Vector-Machines-SVM-with-quantitative-features-EEG-qEEG-
tamaraceranic/Support-Vector-Machines-SVM-with-quantitative-features-EEG-qEEG-
ML competition Classification of Abnormal EEG Background Activity in Newborn Infants
GitHub Repo
https://github.com/einstein-time/wine-quality-prediction-in-R
einstein-time/wine-quality-prediction-in-R
In this R tutorial, we will be estimating the quality of wines with regression trees and model trees. Machine learning has been used to discover key differences in the chemical composition of wines from different regions or to identify the chemical factors that lead a wine to taste sweeter. In most cases, wine experts rate wine that can predict whether the wine is labeled as the bottom or top shelf. The methods used will be regression trees and model trees to create a system capable of mimicking ratings of wine. This will allow the winemakers to identify the key factors that contribute to better-rated wines. The rpart() will be used to specify quality as the outcome variable and use the dot notation to allow all the other columns in the wine_train data frame to be used in predictors.fallen.leaves() addition to the decision tree This addition will show visualizations with the dissemination of regression tree results, as they are readily understood even without a mathematics background. The lead nodes are predicted values for the examples reaching that node. Correlation We can now check the correlation between the predicted and actual quality values provides a simple way to gauge the model’s performance. As one can see that the outcome is 0.54, which is acceptable but not ideal. This correlation only measures how strong the predictions are related to the true value. This is not a measure of how far off the predictions were from the true values. Conclusion: Decision trees were used for numeric prediction to model the wine data. The model trees, which builds a regression model at each leaf node in a hybrid approach. However, the latest cor() did not improve much, it did surpass the performance of the neural network model published. Our model output was close to the published mean absolute error value of 0.45 for the support vector machine model. This was a much simpler learning method for the data used.
GitHub Repo
https://github.com/davidabu91/quote-generator
davidabu91/quote-generator
A random quote generator. Methodologies and technologies I used: Asynchronous fetch request to rest API. Hero patterns vector background. Font awesome. Custom Google font. Twitter web intent URL. Responsive project.
GitHub Repo
https://github.com/harryson22102004/AI-Powered-Knowledge-OS-Enterprise-SaaS-
harryson22102004/AI-Powered-Knowledge-OS-Enterprise-SaaS-
A full-stack AI-powered personal knowledge system with: RAG (Retrieval Augmented Generation) Semantic search Vector DB Role-based authentication Document ingestion AI Q&A Dashboard analytics Background processing Dockerized microservices
GitHub Repo
https://github.com/fluxion9/A-GENTLE-INTRODUCTION-TO-QUANTUM-COMPUTING-