Best AI tools for< Explain Data >

Rerun is an SDK, time-series database, and visualizer for temporal and multimodal data. It is used in fields like robotics, spatial computing, 2D/3D simulation, and finance to verify, debug, and explain data. Rerun allows users to log data like tensors, point clouds, and text to create streams, visualize and interact with live and recorded streams, build layouts, customize visualizations, and extend data and UI functionalities. The application provides a composable data model, dynamic schemas, and custom views for enhanced data visualization and analysis.

This tool uses AI to explain any piece of code you don't understand. Simply paste the code in the code editor and press "Explain Code" and AI will output a paragraph explaining what the code is doing.

ExplainDev is a platform that allows users to ask and answer technical coding questions. It uses computer vision to retrieve technical context from images or videos. The platform is designed to help developers get the best answers to their technical questions and guide others to theirs.

Whybug is an AI tool designed to help developers debug their code by explaining errors. It utilizes a large language model trained on data from StackExchange and other sources to predict the causes of errors and provide solutions. Users can input error messages and receive explanations along with example fixes in code.

ChatDOC is an AI-powered tool that allows users to chat with PDF documents and get instant answers with cited sources. It can summarize long documents, explain complex concepts, and find key information in seconds. ChatDOC is built for professionals and is used by over 500,000 global users.

Fiddler AI is an AI Observability platform that provides tools for monitoring, explaining, and improving the performance of AI models. It offers a range of capabilities, including explainable AI, NLP and CV model monitoring, LLMOps, and security features. Fiddler AI helps businesses to build and deploy high-performing AI solutions at scale.

Formularizer is an AI-powered assistant designed to help users with formula-related tasks in spreadsheets like Excel, Google Sheets, and Notion. It provides step-by-step guidance, formula generation, and explanations to simplify complex formula creation and problem-solving. With support for regular expressions, Excel VBA, and Google Apps Script, Formularizer aims to enhance productivity and make data manipulation more accessible.

Formularizer is an AI-powered assistant that helps users create formulas in Excel, Google Sheets, and Notion. It supports a variety of formula types, including Excel, Google Apps Script, and regular expressions. Formularizer can generate formulas from natural language instructions, explain how formulas work, and even help users debug their formulas. It is designed to be user-friendly and accessible to everyone, regardless of their level of expertise.

Deepnote is an AI-powered analytics and data science notebook platform designed for teams. It allows users to turn notebooks into powerful data apps and dashboards, combining Python, SQL, R, or even working without writing code at all. With Deepnote, users can query various data sources, generate code, explain code, and create interactive visualizations effortlessly. The platform offers features like collaborative workspaces, scheduling notebooks, deploying APIs, and integrating with popular data warehouses and databases. Deepnote prioritizes security and compliance, providing users with control over data access and encryption. It is loved by a community of data professionals and widely used in universities and by data analysts and scientists.

Formulas HQ is an AI-powered formula and script generator for Excel and Sheets. It provides users with a range of tools to simplify complex calculations, automate tasks, and enhance their spreadsheet mastery. With Formulas HQ, users can generate formulas, regular expressions, VBA code, and Apps Script, even without prior programming experience. The platform also offers a chat feature with system prompts to assist users with idea generation and troubleshooting. Formulas HQ is designed to empower users to work smarter and make better business decisions.

Formulas HQ is an AI-powered formula and script generator for Excel and Sheets. It provides users with a variety of tools to simplify complex calculations, automate tasks, and gain insights from data. The platform includes features such as formula generation, regular expression simplification, VBA and Apps Script automation, and chat-based assistance. Formulas HQ is designed to help users improve their productivity and efficiency when working with spreadsheets.

Jam is a bug-tracking tool that helps developers reproduce and debug issues quickly and easily. It automatically captures all the information engineers need to debug, including device and browser information, console logs, network logs, repro steps, and backend tracing. Jam also integrates with popular tools like GitHub, Jira, Linear, Slack, ClickUp, Asana, Sentry, Figma, Datadog, Gitlab, Notion, and Airtable. With Jam, developers can save time and effort by eliminating the need to write repro steps and manually collect information. Jam is used by over 90,000 developers and has received over 150 positive reviews.

TLDR is an AI-powered IDE plugin that explains code in plain English. It supports almost all programming languages and helps developers understand complex code by providing quick summaries. The plugin is available in free and paid versions, offering explanations for regular expressions, SQL queries, and codebases. TLDR aims to save time and enhance code comprehension for individuals and organizations, making it easier to work with unfamiliar code and improve productivity.

Sider.ai is an AI-powered platform that focuses on verifying human users for security purposes. It ensures the authenticity of users by reviewing the security of their connection before granting access. The platform utilizes advanced algorithms to detect and prevent fraudulent activities, providing a secure environment for online interactions. Sider.ai prioritizes user safety and data protection, offering a seamless verification process to enhance security measures.

FormulaGenerator is a free AI-powered toolkit that helps users generate Excel formulas, VBA automations, and SQL queries. It also offers features like error spotting, formula explanations, code generation, and an AnswerBot powered by ChatGPT. The tool is available as a web app and a Google Sheets extension.

DB Sensei is an AI-powered SQL tool that helps developers generate, fix, explain, and format SQL queries with ease. It features a user-friendly interface, AI-driven query generation, query fixing, query explaining, and query formatting. DB Sensei is designed for developers, database administrators, and students who want to get faster results and improve their database skills.

CodeSquire is an AI-powered code writing assistant that helps data scientists, engineers, and analysts write code faster and more efficiently. It provides code completions and suggestions as you type, and can even generate entire functions and SQL queries. CodeSquire is available as a Chrome extension and works with Google Colab, BigQuery, and JupyterLab.

Text2SQL.AI is an AI-powered SQL query builder that helps users generate optimized SQL queries effortlessly. It supports various AI-powered services, including SQL query building from textual instructions, SQL query explanation to plain English, SQL query error fixation, adding custom database schemas, SQL dialects for various database types, Microsoft Excel and Google Sheets formula generation and explanation, and Regex expression generation and explanation. The tool is designed to improve SQL skills, save time, and assist beginners, data analysts, data scientists, data engineers, and software developers in their work.

AI Query is a powerful tool that allows users to generate SQL queries in seconds using simple English. With AI Query, anyone can create efficient SQL queries, without even knowing a thing about it. AI Query is easy to use and affordable, making it a great choice for businesses of all sizes.

Mikie.AI is an AI-powered tool that allows users to generate SQL queries in seconds using natural language. It simplifies the process of creating efficient SQL queries by leveraging AI technology. Users can define database schemas easily, translate SQL to English, and benefit from simple pricing plans. Mikie.AI aims to make SQL query generation error-free and accessible to all types of users, even those with limited SQL knowledge.

imodelsX is a Scikit-learn friendly library that provides tools for explaining, predicting, and steering text models/data. It also includes a collection of utilities for getting started with text data. **Explainable modeling/steering** | Model | Reference | Output | Description | |---|---|---|---| | Tree-Prompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/tree_prompt) | Explanation + Steering | Generates a tree of prompts to steer an LLM (_Official_) | | iPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/iprompt) | Explanation + Steering | Generates a prompt that explains patterns in data (_Official_) | | AutoPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/autoprompt) | Explanation + Steering | Find a natural-language prompt using input-gradients (⌛ In progress)| | D3 | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/d3) | Explanation | Explain the difference between two distributions | | SASC | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/sasc) | Explanation | Explain a black-box text module using an LLM (_Official_) | | Aug-Linear | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_linear) | Linear model | Fit better linear model using an LLM to extract embeddings (_Official_) | | Aug-Tree | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_tree) | Decision tree | Fit better decision tree using an LLM to expand features (_Official_) | **General utilities** | Model | Reference | |---|---| | LLM wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/llm) | Easily call different LLMs | | | Dataset wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/data) | Download minimially processed huggingface datasets | | | Bag of Ngrams | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/bag_of_ngrams) | Learn a linear model of ngrams | | | Linear Finetune | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/linear_finetune) | Finetune a single linear layer on top of LLM embeddings | | **Related work** * [imodels package](https://github.com/microsoft/interpretml/tree/main/imodels) (JOSS 2021) - interpretable ML package for concise, transparent, and accurate predictive modeling (sklearn-compatible). * [Adaptive wavelet distillation](https://arxiv.org/abs/2111.06185) (NeurIPS 2021) - distilling a neural network into a concise wavelet model * [Transformation importance](https://arxiv.org/abs/1912.04938) (ICLR 2020 workshop) - using simple reparameterizations, allows for calculating disentangled importances to transformations of the input (e.g. assigning importances to different frequencies) * [Hierarchical interpretations](https://arxiv.org/abs/1807.03343) (ICLR 2019) - extends CD to CNNs / arbitrary DNNs, and aggregates explanations into a hierarchy * [Interpretation regularization](https://arxiv.org/abs/2006.14340) (ICML 2020) - penalizes CD / ACD scores during training to make models generalize better * [PDR interpretability framework](https://www.pnas.org/doi/10.1073/pnas.1814225116) (PNAS 2019) - an overarching framewwork for guiding and framing interpretable machine learning

Quantus is a toolkit designed for the evaluation of neural network explanations. It offers more than 30 metrics in 6 categories for eXplainable Artificial Intelligence (XAI) evaluation. The toolkit supports different data types (image, time-series, tabular, NLP) and models (PyTorch, TensorFlow). It provides built-in support for explanation methods like captum, tf-explain, and zennit. Quantus is under active development and aims to provide a comprehensive set of quantitative evaluation metrics for XAI methods.

Neural is a Vim and Neovim plugin that integrates various machine learning tools to assist users in writing code, generating text, and explaining code or paragraphs. It supports multiple machine learning models, focuses on privacy, and is compatible with Vim 8.0+ and Neovim 0.8+. Users can easily configure Neural to interact with third-party machine learning tools, such as OpenAI, to enhance code generation and completion. The plugin also provides commands like `:NeuralExplain` to explain code or text and `:NeuralStop` to stop Neural from working. Neural is maintained by the Dense Analysis team and comes with a disclaimer about sending input data to third-party servers for machine learning queries.

Auto Data is a library designed for the automatic generation of realistic datasets, essential for the fine-tuning of Large Language Models (LLMs). This highly efficient and lightweight library enables the swift and effortless creation of comprehensive datasets across various topics, regardless of their size. It addresses challenges encountered during model fine-tuning due to data scarcity and imbalance, ensuring models are trained with sufficient examples.

This course has 18 lessons. Each lesson covers its own topic so start wherever you like! Lessons are labeled either "Learn" lessons explaining a Generative AI concept or "Build" lessons that explain a concept and code examples in both **Python** and **TypeScript** when possible. Each lesson also includes a "Keep Learning" section with additional learning tools. **What You Need** * Access to the Azure OpenAI Service **OR** OpenAI API - _Only required to complete coding lessons_ * Basic knowledge of Python or Typescript is helpful - *For absolute beginners check out these Python and TypeScript courses. * A Github account to fork this entire repo to your own GitHub account We have created a **Course Setup** lesson to help you with setting up your development environment. Don't forget to star (🌟) this repo to find it easier later. ## 🧠 Ready to Deploy? If you are looking for more advanced code samples, check out our collection of Generative AI Code Samples in both **Python** and **TypeScript**. ## 🗣️ Meet Other Learners, Get Support Join our official AI Discord server to meet and network with other learners taking this course and get support. ## 🚀 Building a Startup? Sign up for Microsoft for Startups Founders Hub to receive **free OpenAI credits** and up to **$150k towards Azure credits to access OpenAI models through Azure OpenAI Services**. ## 🙏 Want to help? Do you have suggestions or found spelling or code errors? Raise an issue or Create a pull request ## 📂 Each lesson includes: * A short video introduction to the topic * A written lesson located in the README * Python and TypeScript code samples supporting Azure OpenAI and OpenAI API * Links to extra resources to continue your learning ## 🗃️ Lessons | | Lesson Link | Description | Additional Learning | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | | 00 | Course Setup | **Learn:** How to Setup Your Development Environment | Learn More | | 01 | Introduction to Generative AI and LLMs | **Learn:** Understanding what Generative AI is and how Large Language Models (LLMs) work. | Learn More | | 02 | Exploring and comparing different LLMs | **Learn:** How to select the right model for your use case | Learn More | | 03 | Using Generative AI Responsibly | **Learn:** How to build Generative AI Applications responsibly | Learn More | | 04 | Understanding Prompt Engineering Fundamentals | **Learn:** Hands-on Prompt Engineering Best Practices | Learn More | | 05 | Creating Advanced Prompts | **Learn:** How to apply prompt engineering techniques that improve the outcome of your prompts. | Learn More | | 06 | Building Text Generation Applications | **Build:** A text generation app using Azure OpenAI | Learn More | | 07 | Building Chat Applications | **Build:** Techniques for efficiently building and integrating chat applications. | Learn More | | 08 | Building Search Apps Vector Databases | **Build:** A search application that uses Embeddings to search for data. | Learn More | | 09 | Building Image Generation Applications | **Build:** A image generation application | Learn More | | 10 | Building Low Code AI Applications | **Build:** A Generative AI application using Low Code tools | Learn More | | 11 | Integrating External Applications with Function Calling | **Build:** What is function calling and its use cases for applications | Learn More | | 12 | Designing UX for AI Applications | **Learn:** How to apply UX design principles when developing Generative AI Applications | Learn More | | 13 | Securing Your Generative AI Applications | **Learn:** The threats and risks to AI systems and methods to secure these systems. | Learn More | | 14 | The Generative AI Application Lifecycle | **Learn:** The tools and metrics to manage the LLM Lifecycle and LLMOps | Learn More | | 15 | Retrieval Augmented Generation (RAG) and Vector Databases | **Build:** An application using a RAG Framework to retrieve embeddings from a Vector Databases | Learn More | | 16 | Open Source Models and Hugging Face | **Build:** An application using open source models available on Hugging Face | Learn More | | 17 | AI Agents | **Build:** An application using an AI Agent Framework | Learn More | | 18 | Fine-Tuning LLMs | **Learn:** The what, why and how of fine-tuning LLMs | Learn More |

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

The Vertex AI SDK for Python is a library that provides a convenient way to use the Vertex AI API. It offers a high-level interface for creating and managing Vertex AI resources, such as datasets, models, and endpoints. The SDK also provides support for training and deploying custom models, as well as using AutoML models. With the Vertex AI SDK for Python, you can quickly and easily build and deploy machine learning models on Vertex AI.

The ChatGPT R package provides a set of features to assist in R coding. It includes addins like Ask ChatGPT, Comment selected code, Complete selected code, Create unit tests, Create variable name, Document code, Explain selected code, Find issues in the selected code, Optimize selected code, and Refactor selected code. Users can interact with ChatGPT to get code suggestions, explanations, and optimizations. The package helps in improving coding efficiency and quality by providing AI-powered assistance within the RStudio environment.

LLMonFHIR is an iOS application that utilizes large language models (LLMs) to interpret and provide context around patient data in the Fast Healthcare Interoperability Resources (FHIR) format. It connects to the OpenAI GPT API to analyze FHIR resources, supports multiple languages, and allows users to interact with their health data stored in the Apple Health app. The app aims to simplify complex health records, provide insights, and facilitate deeper understanding through a conversational interface. However, it is an experimental app for informational purposes only and should not be used as a substitute for professional medical advice. Users are advised to verify information provided by AI models and consult healthcare professionals for personalized advice.

The W3C Cognitive AI Community Group focuses on advancing Cognitive AI through collaboration on defining use cases, open source implementations, and application areas. The group aims to demonstrate the potential of Cognitive AI in various domains such as customer services, healthcare, cybersecurity, online learning, autonomous vehicles, manufacturing, and web search. They work on formal specifications for chunk data and rules, plausible knowledge notation, and neural networks for human-like AI. The group positions Cognitive AI as a combination of symbolic and statistical approaches inspired by human thought processes. They address research challenges including mimicry, emotional intelligence, natural language processing, and common sense reasoning. The long-term goal is to develop cognitive agents that are knowledgeable, creative, collaborative, empathic, and multilingual, capable of continual learning and self-awareness.

This repository contains slides, notebooks, and data for the Machine Learning University (MLU) Responsible AI class. The mission is to make Machine Learning accessible to everyone, covering widely used ML techniques and applying them to real-world problems. The class includes lectures, final projects, and interactive visuals to help users learn about Responsible AI and core ML concepts.

This repository contains a collection of AI projects covering various areas of machine learning. Each project is accompanied by detailed articles on the associated blog sciblog. Projects range from introductory topics like Convolutional Neural Networks and Transfer Learning to advanced topics like Fraud Detection and Recommendation Systems. The repository also includes tutorials on data generation, distributed training, natural language processing, and time series forecasting. Additionally, it features visualization projects such as football match visualization using Datashader.

Greenplum Database (GPDB) is an advanced, fully featured, open source data warehouse, based on PostgreSQL. It provides powerful and rapid analytics on petabyte scale data volumes. Uniquely geared toward big data analytics, Greenplum Database is powered by the world’s most advanced cost-based query optimizer delivering high analytical query performance on large data volumes.

A massive list including a huge amount of products and services that are completely free! ⭐ Star on GitHub • 🤝 Contribute # Table of Contents * APIs, Data & ML * Artificial Intelligence * BaaS * Code Editors * Code Generation * DNS * Databases * Design & UI * Domains * Email * Font * For Students * Forms * Linux Distributions * Messaging & Streaming * PaaS * Payments & Billing * SSL

This repository provides a deployment guide for utilizing Google Cloud's Generative AI tools in marketing scenarios. It includes step-by-step instructions, examples of crafting marketing materials, and supplementary Jupyter notebooks. The demos cover marketing insights, audience analysis, trendspotting, content search, content generation, and workspace integration. Users can access and visualize marketing data, analyze trends, improve search experience, and generate compelling content. The repository structure includes backend APIs, frontend code, sample notebooks, templates, and installation scripts.

BIA `bob` is a Jupyter-based assistant for interacting with data using large language models to generate Python code. It can utilize OpenAI's chatGPT, Google's Gemini, Helmholtz' blablador, and Ollama. Users need respective accounts to access these services. Bob can assist in code generation, bug fixing, code documentation, GPU-acceleration, and offers a no-code custom Jupyter Kernel. It provides example notebooks for various tasks like bio-image analysis, model selection, and bug fixing. Installation is recommended via conda/mamba environment. Custom endpoints like blablador and ollama can be used. Google Cloud AI API integration is also supported. The tool is extensible for Python libraries to enhance Bob's functionality.

Lightning Lab is a public template for artificial intelligence and machine learning research projects using Lightning AI's PyTorch Lightning. It provides a structured project layout with modules for command line interface, experiment utilities, Lightning Module and Trainer, data acquisition and preprocessing, model serving APIs, project configurations, training checkpoints, technical documentation, logs, notebooks for data analysis, requirements management, testing, and packaging. The template simplifies the setup of deep learning projects and offers extras for different domains like vision, text, audio, reinforcement learning, and forecasting.

This repository contains codes related to Generative AI as per YouTube video. It includes various notebooks and files for different days covering topics like map reduce, text to SQL, LLM parameters, tagging, and Kaggle competition. The repository also includes resources like PDF files and databases for different projects related to Generative AI.

Yet Another Applied LLM Benchmark is a collection of diverse tests designed to evaluate the capabilities of language models in performing real-world tasks. The benchmark includes tests such as converting code, decompiling bytecode, explaining minified JavaScript, identifying encoding formats, writing parsers, and generating SQL queries. It features a dataflow domain-specific language for easily adding new tests and has nearly 100 tests based on actual scenarios encountered when working with language models. The benchmark aims to assess whether models can effectively handle tasks that users genuinely care about.

MemFree is an open-source hybrid AI search engine that allows users to simultaneously search their personal knowledge base (bookmarks, notes, documents, etc.) and the Internet. It features a self-hosted super fast serverless vector database, local embedding and rerank service, one-click Chrome bookmarks index, and full code open source. Users can contribute by opening issues for bugs or making pull requests for new features or improvements.

Expert in explaining data visuals for academic publishing.

I clean and explain data.

Expert in generating and debugging Neo4j code, with explanations on graph database principles.

Master the art of Google Sheets with an assistant who can do everything from answer questions about basic features, explain functions in an eloquent and succinct manner, simplify the most complex formulas into easy steps, and help you identify techniques to effectively visualize your data.

Explain using math and python

Generate and explain regex patterns from your description, it support English and Chinese.

I generate custom Notion formulas and explain how to use them.

I simplify and explain AI concepts.

This GPT helps explain the findings in the State of Webhooks Report

I explain AI papers in Chinese.

I explain basic concepts and analyze code in distributed systems.

I will explain any concept in calculus in a fun way!

I create and explain ARIS diagrams.

I offer engaging Python puzzles, explain solutions and immediately present the next challenge.

give me code example without explain

Guiding you from beginner to expert in Competitive Programming, or simply explain solutions to your problems.

Explains ML and data science concepts clearly, catering to various expertise levels.

Produces Streamlit code first, then explains briefly in a casual, supportive tone.

I create visuals to explain complex crypto concepts.

Creates data schemas and writes SQL queries for developers