Best AI tools for< generate machine learning data >

AI Placeholder is a free AI-Powered Fake or Dummy Data API for testing and prototyping. We use OpenAI API to generate dummy content. You can directly use the hosted version, or self-host it yourself.

Cogitotech is an AI tool that specializes in data annotation and labeling expertise. The platform offers a comprehensive suite of services tailored to meet training data needs for computer vision models and AI applications. With a decade-long industry exposure, Cogitotech provides high-quality training data for industries like healthcare, financial services, security, and more. The platform helps minimize biases in AI algorithms and ensures accurate and reliable training data solutions for deploying AI in real-life systems.

GptDemo.Net is a website that provides a directory of AI tools and resources. The website includes a search engine that allows users to find AI tools based on their needs. GptDemo.Net also provides news and updates on the latest AI developments.

Omneky is an AI-powered platform that helps businesses generate creative ads at scale. It uses machine learning to analyze data and generate personalized content that is both brand-safe and visually stunning. Omneky also provides tools for approving and launching creatives, as well as insights into creative performance.

Pluto.fi is an AI-powered investment platform that provides users with personalized investment advice, research, and trading tools. The platform uses natural language processing and machine learning to analyze data from a variety of sources, including news articles, financial reports, and social media. This data is then used to generate insights and recommendations that are tailored to each user's individual needs and goals. Pluto.fi also offers a variety of features that make it easy for users to manage their investments, including a portfolio tracker, a stock screener, and a news feed. The platform is available as a web application and a mobile app.

Nextatlas Generate is a generative trend forecasting service that uses advanced natural language processing and machine learning algorithms to analyze vast amounts of data and predict future trends in a wide range of industries and markets. It is powered by GPT-4 and the Nextatlas engine, which analyzes data from over 300k early adopters on social media to identify emerging trends. Generate can create high-quality, human-like content, such as blog articles, social media posts, and trend reports, tailored to specific industries and topics. It also features a Casefinder, an AI-driven matchmaker for business cases, and visualization tools for effective data interpretation.

Qlik AutoML is an AI tool that offers automated machine learning for analytics teams. It allows users to create machine learning experiments, identify key drivers in data, train models, and make predictions. With a focus on no-code machine learning, Qlik AutoML simplifies the process of generating predictive models and understanding outcomes. The tool enables users to explore predictive data, test what-if scenarios, and leverage AI-powered connectors for seamless integration with other AI and machine learning tools.

Datagen is a platform that provides synthetic data for computer vision. Synthetic data is artificially generated data that can be used to train machine learning models. Datagen's data is generated using a variety of techniques, including 3D modeling, computer graphics, and machine learning. The company's data is used by a variety of industries, including automotive, security, smart office, fitness, cosmetics, and facial applications.

Fine-Tune AI is a tool that allows users to generate fine-tune data sets using prompts. This can be useful for a variety of tasks, such as improving the accuracy of machine learning models or creating new training data for AI applications.

Text Generator is an AI-powered text generation tool that provides users with accurate, fast, and flexible text generation capabilities. With its advanced large neural networks, Text Generator offers a cost-effective solution for various text-related tasks. The tool's intuitive 'prompt engineering' feature allows users to guide text creation by providing keywords and natural questions, making it adaptable for tasks such as classification and sentiment analysis. Text Generator ensures industry-leading security by never storing personal information on its servers. The tool's continuous training ensures that its AI remains up-to-date with the latest events. Additionally, Text Generator offers a range of features including speech-to-text API, text-to-speech API, and code generation, supporting multiple spoken languages and programming languages. With its one-line migration from OpenAI's text generation hub and a shared embedding for multiple spoken languages, images, and code, Text Generator empowers users with powerful search, fingerprinting, tracking, and classification capabilities.

AppTek is a global leader in artificial intelligence (AI) and machine learning (ML) technologies for automatic speech recognition (ASR), neural machine translation (NMT), natural language processing/understanding (NLP/U) and text-to-speech (TTS) technologies. The AppTek platform delivers industry-leading solutions for organizations across a breadth of global markets such as media and entertainment, call centers, government, enterprise business, and more. Built by scientists and research engineers who are recognized among the best in the world, AppTek’s solutions cover a wide array of languages/ dialects, channels, domains and demographics.

MOSTLY AI is a synthetic data generation platform that provides high-quality, privacy-safe synthetic versions of your datasets for ML, advanced analytics, software testing, and data sharing. With MOSTLY AI, you can generate synthetic data that is statistically similar to your real data, but without the privacy risks. This makes it possible to share data more freely, collaborate more effectively, and develop better AI models.

Gretel.ai is a multimodal synthetic data platform for developers. It allows users to generate artificial datasets with the same characteristics as real data, so they can develop and test AI models without compromising privacy. Gretel's APIs make it simple to generate anonymized and safe synthetic data so you can innovate faster and preserve privacy while doing it.

Rapid AI DAta Yields (RAIDAY) is a platform that provides AI tools, data products, and educational resources to help businesses and individuals leverage the power of artificial intelligence. RAIDAY's mission is to democratize and streamline the creation of simple yet powerful AI and data products for everyone, regardless of their technical expertise or resources. The platform offers a range of AI tools, including content generators, data analysis tools, and AI-powered chatbots. RAIDAY also provides a library of AI-generated content and data products that can be used to train AI models or to create new AI applications. In addition to its AI tools and data products, RAIDAY also offers a variety of educational resources, including tutorials, webinars, and blog posts, to help users learn about AI and how to use it effectively.

Synthesis AI is a synthetic data platform that enables more capable and ethical computer vision AI. It provides on-demand labeled images and videos, photorealistic images, and 3D generative AI to help developers build better models faster. Synthesis AI's products include Synthesis Humans, which allows users to create detailed images and videos of digital humans with rich annotations; Synthesis Scenarios, which enables users to craft complex multi-human simulations across a variety of environments; and a range of applications for industries such as ID verification, automotive, avatar creation, virtual fashion, AI fitness, teleconferencing, visual effects, and security.

Supertype is a full-cycle data science consultancy offering a range of services including computer vision, custom BI development, managed data analytics, programmatic report generation, and more. They specialize in providing tailored solutions for data analytics, business intelligence, and data engineering services. Supertype also offers services for developing custom web dashboards, computer vision research and development, PDF generation, managed analytics services, and LLM development. Their expertise extends to implementing data science in various industries such as e-commerce, mobile apps & games, and financial markets. Additionally, Supertype provides bespoke solutions for enterprises, advisory and consulting services, and an incubator platform for data scientists and engineers to work on real-world projects.

DataZenith is an AI application that leverages virtual reality (VR) technology to generate realistic and immersive datasets for training AI models. It enables the development of AI algorithms that can understand and interact with virtual environments, improving algorithm accuracy and performance in real-world scenarios. DataZenith offers user-friendly solutions for non-technical users, with features such as realistic VR data generation, addressing edge cases, user-friendly interface, customizable VR environments, and precise VR data annotations.

Hive AI provides a suite of AI models and solutions for understanding, searching, and generating content. Their AI models can be integrated into applications via APIs, enabling developers to add advanced content understanding capabilities to their products. Hive AI's solutions are used by businesses in various industries, including digital platforms, sports, media, and marketing, to streamline content moderation, automate image search and authentication, measure sponsorships, and monetize ad inventory.

Victor Dibia is an expert in Applied Machine Learning and HCI. He is currently a Principal Research Software Engineer at the Human-AI eXperiences (HAX) team, Microsoft Research where he focuses on Generative AI. His research interests are at the intersection of human-computer interaction (HCI), computational social science, and applied machine learning. His research has been published at conferences such as ACL, EMNLP, AAAI, and CHI and has received multiple best paper awards. His work has also been featured in outlets such as the Wall Street Journal and VentureBeat. He is an IEEE Senior member, a Google Certified Professional (Data Engineer, Cloud Architect), and currently a Google Developer Expert in Machine Learning. He holds a PhD in Information Systems from City University of Hong Kong (recipient of the HKPFS scholar award by the Hong Kong Research Grants Council). His dissertation studied developer contribution behavior in software crowdsourcing contests - factors influencing participation, the impact of incentives on participation behavior, and the problem-solving process within crowdsourcing contests. Prior to City University, he studied at the Information Networking Institute at Carnegie Mellon University where he earned a Master's degree in Information Networking. He previously worked as a Principal Research Engineer at Cloudera Fast Forward Labs, Research Staff Member at IBM Research, Technical Lead for MIT Global Startup Labs, Researcher at the Innovation Management Lab, Athens Information Technology Athens Greece, and founder/lead developer for a small startup focused on West African markets.

JADBio is an automated machine learning (AutoML) platform that accelerates biomarker discovery and interpretation for drug discovery and precision medicine. It enables researchers and scientists to analyze multi-omics data, including genomics, transcriptome, metagenome, proteome, metabolome, phenotype/clinical data, and images, to identify biomarkers and gain insights into disease mechanisms and treatment response. JADBio's AutoML platform automates the machine learning process, making it accessible to users with limited technical expertise. It provides a user-friendly interface, pre-built machine learning models, and guided workflows to help researchers quickly and efficiently analyze their data and generate meaningful results.

The RAG Experiment Accelerator is a versatile tool that helps you conduct experiments and evaluations using Azure AI Search and RAG pattern. It offers a rich set of features, including experiment setup, integration with Azure AI Search, Azure Machine Learning, MLFlow, and Azure OpenAI, multiple document chunking strategies, query generation, multiple search types, sub-querying, re-ranking, metrics and evaluation, report generation, and multi-lingual support. The tool is designed to make it easier and faster to run experiments and evaluations of search queries and quality of response from OpenAI, and is useful for researchers, data scientists, and developers who want to test the performance of different search and OpenAI related hyperparameters, compare the effectiveness of various search strategies, fine-tune and optimize parameters, find the best combination of hyperparameters, and generate detailed reports and visualizations from experiment results.

Awesome MLOps is a curated list of tools related to Machine Learning Operations, covering areas such as AutoML, CI/CD for Machine Learning, Data Cataloging, Data Enrichment, Data Exploration, Data Management, Data Processing, Data Validation, Data Visualization, Drift Detection, Feature Engineering, Feature Store, Hyperparameter Tuning, Knowledge Sharing, Machine Learning Platforms, Model Fairness and Privacy, Model Interpretability, Model Lifecycle, Model Serving, Model Testing & Validation, Optimization Tools, Simplification Tools, Visual Analysis and Debugging, and Workflow Tools. The repository provides a comprehensive collection of tools and resources for individuals and teams working in the field of MLOps.

The 'llms-tools' repository is a comprehensive collection of AI tools, open-source projects, and research related to Large Language Models (LLMs) and Chatbots. It covers a wide range of topics such as AI in various domains, open-source models, chats & assistants, visual language models, evaluation tools, libraries, devices, income models, text-to-image, computer vision, audio & speech, code & math, games, robotics, typography, bio & med, military, climate, finance, and presentation. The repository provides valuable resources for researchers, developers, and enthusiasts interested in exploring the capabilities of LLMs and related technologies.

Recent LLM (Large Language Models)-based CV and multi-modal works.

Langroid is a Python framework that makes it easy to build LLM-powered applications. It uses a multi-agent paradigm inspired by the Actor Framework, where you set up Agents, equip them with optional components (LLM, vector-store and tools/functions), assign them tasks, and have them collaboratively solve a problem by exchanging messages. Langroid is a fresh take on LLM app-development, where considerable thought has gone into simplifying the developer experience; it does not use Langchain.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

This repository provides a systematic and comprehensive review of efficient LLMs research. We organize the literature in a taxonomy consisting of three main categories, covering distinct yet interconnected efficient LLMs topics from **model-centric** , **data-centric** , and **framework-centric** perspective, respectively. We hope our survey and this GitHub repository can serve as valuable resources to help researchers and practitioners gain a systematic understanding of the research developments in efficient LLMs and inspire them to contribute to this important and exciting field.

LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) training with masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine-tuned to achieve state-of-the-art performance.

AIDE is a machine learning code generation agent that can generate solutions for machine learning tasks from natural language descriptions. It has the following features: 1. **Instruct with Natural Language**: Describe your problem or additional requirements and expert insights, all in natural language. 2. **Deliver Solution in Source Code**: AIDE will generate Python scripts for the **tested** machine learning pipeline. Enjoy full transparency, reproducibility, and the freedom to further improve the source code! 3. **Iterative Optimization**: AIDE iteratively runs, debugs, evaluates, and improves the ML code, all by itself. 4. **Visualization**: We also provide tools to visualize the solution tree produced by AIDE for a better understanding of its experimentation process. This gives you insights not only about what works but also what doesn't. AIDE has been benchmarked on over 60 Kaggle data science competitions and has demonstrated impressive performance, surpassing 50% of Kaggle participants on average. It is particularly well-suited for tasks that require complex data preprocessing, feature engineering, and model selection.

This repository contains a collection of papers and resources related to Large Language Models (LLMs), including their applications in various domains such as text generation, translation, question answering, and dialogue systems. The repository also includes discussions on the ethical and societal implications of LLMs. **Description** This repository is a collection of papers and resources related to Large Language Models (LLMs). LLMs are a type of artificial intelligence (AI) that can understand and generate human-like text. They have a wide range of applications, including text generation, translation, question answering, and dialogue systems. **For Jobs** - **Content Writer** - **Copywriter** - **Editor** - **Journalist** - **Marketer** **AI Keywords** - **Large Language Models** - **Natural Language Processing** - **Machine Learning** - **Artificial Intelligence** - **Deep Learning** **For Tasks** - **Generate text** - **Translate text** - **Answer questions** - **Engage in dialogue** - **Summarize text**

The 'nlp-llms-resources' repository is a comprehensive resource list for Natural Language Processing (NLP) and Large Language Models (LLMs). It covers a wide range of topics including traditional NLP datasets, data acquisition, libraries for NLP, neural networks, sentiment analysis, optical character recognition, information extraction, semantics, topic modeling, multilingual NLP, domain-specific LLMs, vector databases, ethics, costing, books, courses, surveys, aggregators, newsletters, papers, conferences, and societies. The repository provides valuable information and resources for individuals interested in NLP and LLMs.

Awesome-Segment-Anything is a powerful tool for segmenting and extracting information from various types of data. It provides a user-friendly interface to easily define segmentation rules and apply them to text, images, and other data formats. The tool supports both supervised and unsupervised segmentation methods, allowing users to customize the segmentation process based on their specific needs. With its versatile functionality and intuitive design, Awesome-Segment-Anything is ideal for data analysts, researchers, content creators, and anyone looking to efficiently extract valuable insights from complex datasets.

This repository does not provide a tool, but rather a collection of resources and strategies for academics in the field of artificial intelligence who are feeling depressed or overwhelmed by the rapid advancements in the field. The resources include articles, blog posts, and other materials that offer advice on how to cope with the challenges of working in a fast-paced and competitive environment.

This repository is a curated list of research papers that explore the integration of Large Language Model (LLM) technology with tabular data. It aims to provide a comprehensive resource for researchers and practitioners interested in this emerging field. The repository includes papers on a wide range of topics, including table-to-text generation, table question answering, and tabular data classification. It also includes a section on related datasets and resources.

Data science co-pilot specializing in statistical modeling and machine learning.

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I'm your Machine Learning and Python mentor, ready to teach and guide you.

Humorous AI critic known for skepticism, contradictory arguments, and combining Animal and Machine Learning related Terms.

Expert in medical imaging AI, adept in machine learning tools.

A GPT with Knowledge of the most recent papers in Machine Learning`

I suggest data science projects and give tips on request.

Secure, innovative GPT for collaborative data science and education.

Generate bibtex for a research paper title

A friendly data science coach offering practical, useful, and accurate advice.

Simplifies data science for professionals.

Using OpenLLMLeaderboard data to answer your questions about LLM. For Currently!

Beyond analysis, I deliver insights.

Leading Big Data Analytics tool, blending advanced technology with OpenAI's expertise.

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

AI & ML Expert adept in deep learning frameworks

NeurIPS 2023 Gateway

Realiza consultas de Deep Learning basado en el contenido del canal de Youtube DotCSV

Creating Business Value using AI, Digital, Data

Casual and friendly AI guide for easy learning