296 skills found · Page 5 of 10
byerlikaya / SmartRAGMulti-Modal RAG for .NET — query databases, documents, images and audio in natural language. Production-ready with multi-AI support, vector storage, and multi-database coordination.
aws-samples / Text Embeddings Pipeline For RagA pipeline to convert contextual knowledge stored in documents and databases into text embeddings, and store them in a vector store
maximilien / Weave CliA universal CLI for Weaviate, Milvus, Chroma, Qdrant, and other vector DBs to help view, list, create, delete, and search collections and documents in collections for development, test, and debugging purposes
reddyprasade / Machine Learning Interview PreparationPrepare to Technical Skills Here are the essential skills that a Machine Learning Engineer needs, as mentioned Read me files. Within each group are topics that you should be familiar with. Study Tip: Copy and paste this list into a document and save to your computer for easy referral. Computer Science Fundamentals and Programming Topics Data structures: Lists, stacks, queues, strings, hash maps, vectors, matrices, classes & objects, trees, graphs, etc. Algorithms: Recursion, searching, sorting, optimization, dynamic programming, etc. Computability and complexity: P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc. Computer architecture: Memory, cache, bandwidth, threads & processes, deadlocks, etc. Probability and Statistics Topics Basic probability: Conditional probability, Bayes rule, likelihood, independence, etc. Probabilistic models: Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc. Statistical measures: Mean, median, mode, variance, population parameters vs. sample statistics etc. Proximity and error metrics: Cosine similarity, mean-squared error, Manhattan and Euclidean distance, log-loss, etc. Distributions and random sampling: Uniform, normal, binomial, Poisson, etc. Analysis methods: ANOVA, hypothesis testing, factor analysis, etc. Data Modeling and Evaluation Topics Data preprocessing: Munging/wrangling, transforming, aggregating, etc. Pattern recognition: Correlations, clusters, trends, outliers & anomalies, etc. Dimensionality reduction: Eigenvectors, Principal Component Analysis, etc. Prediction: Classification, regression, sequence prediction, etc.; suitable error/accuracy metrics. Evaluation: Training-testing split, sequential vs. randomized cross-validation, etc. Applying Machine Learning Algorithms and Libraries Topics Models: Parametric vs. nonparametric, decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc. Learning procedure: Linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods; regularization, hyperparameter tuning, etc. Tradeoffs and gotchas: Relative advantages and disadvantages, bias and variance, overfitting and underfitting, vanishing/exploding gradients, missing data, data leakage, etc. Software Engineering and System Design Topics Software interface: Library calls, REST APIs, data collection endpoints, database queries, etc. User interface: Capturing user inputs & application events, displaying results & visualization, etc. Scalability: Map-reduce, distributed processing, etc. Deployment: Cloud hosting, containers & instances, microservices, etc. Move on to the final lesson of this course to find lots of sample practice questions for each topic!
mhowerton91 / History<!DOCTYPE html> <!-- Copyright 2016 Google Inc. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> <html> <head> <meta charset="utf-8"> <title>Chrome Platform Status</title> <meta name="viewport" content="width=device-width, initial-scale=1.0, minimum-scale=1.0"> <link rel="manifest" href="/static/manifest.json"> <meta name="theme-color" content="#366597"> <link rel="icon" sizes="192x192" href="/static/img/crstatus_192.png"> <!-- iOS: run in full-screen mode and display upper status bar as translucent --> <meta name="apple-mobile-web-app-capable" content="yes"> <meta name="mobile-web-app-capable" content="yes"> <meta name="apple-mobile-web-app-status-bar-style" content="black-translucent"> <link rel="apple-touch-icon" href="/static/img/crstatus_128.png"> <link rel="apple-touch-icon-precomposed" href="/static/img/crstatus_128.png"> <link rel="shortcut icon" href="/static/img/crstatus_128.png"> <link rel="preconnect" href="https://www.google-analytics.com" crossorigin> <!-- <link 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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. Code examples are <a href="https://github.com/GoogleChrome/samples/blob/gh-pages/LICENSE">Apache-2.0</a>.</p> <div><a href="https://groups.google.com/a/chromium.org/forum/#!newtopic/blink-dev">File content issue</a> | <a href="https://docs.google.com/a/chromium.org/forms/d/1djZD0COt4NgRwDYesNLkYAb_O8YL39eEvF78vk06R9c/viewform">Request "edit" access</a> | <a href="https://github.com/GoogleChrome/chromium-dashboard/issues">File site bug</a> | <a href="https://docs.google.com/document/d/1jrSlM4Yhae7XCJ8BuasWx71CvDEMMbSKbXwx7hoh1Co/edit?pli=1" target="_blank">About</a> | <a href="https://www.google.com/accounts/ServiceLogin?service=ah&passive=true&continue=https://appengine.google.com/_ah/conflogin%3Fcontinue%3Dhttps://www.chromestatus.com/metrics/css/timeline/animated">Login</a> </div> </footer> </app-drawer-layout> <chromedash-toast msg="Welcome to chromestatus.com!"></chromedash-toast> <script> /*! (c) 2017 Copyright (c) 2016 The Google Inc. All rights reserved. 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symfony / AI StoreLow-level abstraction for storing and retrieving documents in a vector store.
ishaan007 / Vector Space ModellingNLP in python Vector Space Modelling and document classification NLP
nyk510 / Scdv PythonSparse Composite Document Vectors using soft clustering over distributional representations
jina-ai / Executor 3d EncoderAn executor that wraps 3D mesh models and encodes 3D content documents to d-dimension vector.
R3gm / ConversaDocsProgram that enables seamless interaction with your documents through an advanced vector database and the power of Large Language Model (LLM) technology.
michaelcapizzi / Nlp BasicsDemo of embedding words and documents into vector space
fakhirali / DocLLMChat with Documents from scratch using LLMs and a vector databse
Pigeon111111 / SeisLearner RAGRAG Agent System - Spring AI + React + Multi-modal Document Parsing + Hybrid Vector Retrieval
MLWave / Normalized Compression NeighborsDocument or binary file vectorization with Normalized Compression Distance in Python.
CyberAgentAILab / Text2paletteImplementation of "Multimodal Color Recommendation for Vector Graphic Documents" ACM MM'23
koheiw / WordvectorTrain word and document vectors using quanteda
PritiG1 / Multimodal RAGMultimodal RAG with Docling that lets you query PDFs containing text, tables, images, and formulas using a Retrieval-Augmented Generation pipeline. It leverages Docling for structured PDF parsing and Qdrant for fast vector search over embedded document chunks.
easonlai / Chatbot With Pdf StreamlitThis code example shows how to make a chatbot for semantic search over documents using Streamlit, LangChain, and various vector databases. The chatbot lets users ask questions and get answers from a document collection. The code is in Python and can be customized for different scenarios and data.
zakirkun / Rag Chat BotA Retrieval-Augmented Generation (RAG) Chat Bot that provides accurate responses based on available documents. This application uses PostgreSQL with pgvector extension for storing and searching vector embeddings, and OpenAI for generating embeddings and responses.
whmachao / Information Retrieval SystemA prototype of information retrieval system inclduing the document parsing, index construction, query parsing to support the vector space model.
