Best AI tools for< Create Machine Learning Models >

Teachable Machine is a web-based tool that makes it easy to create custom machine learning models, even if you don't have any coding experience. With Teachable Machine, you can train models to recognize images, sounds, and poses. Once you've trained a model, you can export it to use in your own projects.

Experiments with Google is a website that showcases a collection of experiments created by coders using Chrome, Android, AI, AR, and more. The experiments are designed to inspire others to create new experiments and explore the possibilities of these technologies. The website also provides helpful tools and resources for creating experiments.

Incribo is a company that provides synthetic data for training machine learning models. Synthetic data is artificially generated data that is designed to mimic real-world data. This data can be used to train machine learning models without the need for real-world data, which can be expensive and difficult to obtain. Incribo's synthetic data is high quality and affordable, making it a valuable resource for machine learning developers.

RunwayML Experiments is a platform that allows users to create and share machine learning models. It provides a variety of tools and resources to help users get started with machine learning, including a library of pre-trained models, a visual programming interface, and a community of experts. RunwayML Experiments is used by a variety of people, including researchers, students, and hobbyists.

Liner is a free and easy-to-use tool that allows users to train machine learning models without writing any code. It provides a user-friendly interface that guides users through the process of importing data, selecting a model, and training the model. Liner also offers a variety of pre-trained models that can be used for common tasks such as image classification, text classification, and object detection. With Liner, users can quickly and easily create and deploy machine learning applications without the need for specialized knowledge or expertise.

Keras is an open-source deep learning API written in Python, designed to make building and training deep learning models easier. It provides a user-friendly interface and a wide range of features and tools to help developers create and deploy machine learning applications. Keras is compatible with multiple frameworks, including TensorFlow, Theano, and CNTK, and can be used for a variety of tasks, including image classification, natural language processing, and time series analysis.

HappyML is an AI tool designed to assist users in machine learning tasks. It provides a user-friendly interface for running machine learning algorithms without the need for complex coding. With HappyML, users can easily build, train, and deploy machine learning models for various applications. The tool offers a range of features such as data preprocessing, model evaluation, hyperparameter tuning, and model deployment. HappyML simplifies the machine learning process, making it accessible to users with varying levels of expertise.

Stablematic is a web-based platform that allows users to run Stable Diffusion and other machine learning models without the need for local setup or hardware limitations. It provides a user-friendly interface, pre-installed plugins, and dedicated GPU resources for a seamless and efficient workflow. Users can generate images and videos from text prompts, merge multiple models, train custom models, and access a range of pre-trained models, including Dreambooth and CivitAi models. Stablematic also offers API access for developers and dedicated support for users to explore and utilize the capabilities of Stable Diffusion and other machine learning models.

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.

Practical Deep Learning for Coders is a free course designed for individuals with some coding experience who want to learn how to apply deep learning and machine learning to practical problems. The course covers topics such as building and training deep learning models for computer vision, natural language processing, tabular analysis, and collaborative filtering problems. It is based on a 5-star rated book and does not require any special hardware or software. The course is led by Jeremy Howard, a renowned expert in machine learning and the President and Chief Scientist of Kaggle.

WikeAI is an all-in-one AI platform that provides access to top AI models such as GPT-4, Claude3, Mistral, and Llama2. It offers professional-level cross-model integration, allowing users to experience powerful language understanding, speech synthesis, and visual generation technology without switching between multiple systems. WikeAI simplifies the process of using AI for content writing by generating blog articles, product descriptions, social media ads, and more in seconds. The platform offers different pricing plans tailored to various user needs, from casual users to language creators.

WikeAI is an all-in-one AI platform that offers top models like GPT4, Claude3, Mistral, and Llama3. It provides advanced AI capabilities such as conversation simulation, content generation, and more. Users can experience professional-level cross-model integration and benefit from AI-powered content writing, social media ads creation, and product description generation. WikeAI simplifies the use of AI technology with a one-time payment model, making it accessible and cost-effective. The platform supports various AI models and offers fast content generation, unique and original content, and commercial use rights.

Replicate is an AI tool that allows users to run and fine-tune open-source models, deploy custom models at scale, and generate images, text, videos, music, and speech with just one line of code. It provides a platform for the community to contribute and explore thousands of production-ready AI models, enabling users to push the boundaries of AI beyond academic papers and demos. With features like fine-tuning models, deploying custom models, and scaling on Replicate, users can easily create and deploy AI solutions for various tasks.

Gradio is a tool that allows users to quickly and easily create web-based interfaces for their machine learning models. With Gradio, users can share their models with others, allowing them to interact with and use the models remotely. Gradio is easy to use and can be integrated with any Python library. It can be used to create a variety of different types of interfaces, including those for image classification, natural language processing, and time series analysis.

Moonvalley is a research company focused on developing cutting-edge generative media technologies. The team consists of top researchers, engineers, and artists with backgrounds in leading tech companies. Moonvalley specializes in advanced video and image machine learning models, aiming to shape the future of media creation.

Datumbox is a machine learning platform that offers a powerful open-source Machine Learning Framework written in Java. It provides a large collection of algorithms, models, statistical tests, and tools to power up intelligent applications. The platform enables developers to build smart software and services quickly using its REST Machine Learning API. Datumbox API offers off-the-shelf Classifiers and Natural Language Processing services for applications like Sentiment Analysis, Topic Classification, Language Detection, and more. It simplifies the process of designing and training Machine Learning models, making it easy for developers to create innovative applications.

Fathom5 is a company that specializes in the intersection of AI and industrial systems. They offer a range of products and services to help customers build more resilient, flexible, and efficient industrial systems. Fathom5's approach is unique in that they take a security-first approach to cyber-physical system design. This means that security is built into every stage of the development process, from ideation to engineering to testing to deployment. This approach has been proven to achieve higher system resiliency and faster regulatory compliance at a reduced cost.

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.

Streamlit is a web application framework that allows users to create interactive web applications with Python. It enables data scientists and developers to easily build and share data-driven applications. With Streamlit, users can create interactive visualizations, dashboards, and machine learning models without the need for extensive web development knowledge. The platform simplifies the process of turning data scripts into shareable web apps, making it a valuable tool for data science projects, prototyping, and showcasing insights.

Type AI is an AI-powered keyboard that allows users to write with the assistance of the latest deep learning models. It can be used in any app, making it a versatile tool for a variety of writing tasks. Type AI is designed to help users write more efficiently and effectively, by providing them with suggestions for words, phrases, and even entire sentences. It can also be used to translate text, check grammar, and detect plagiarism.

This repository is a compilation of excellent ML and DL tutorials created by various individuals and organizations. It covers a wide range of topics, including machine learning fundamentals, deep learning, computer vision, natural language processing, reinforcement learning, and more. The resources are organized into categories, making it easy to find the information you need. Whether you're a beginner or an experienced practitioner, you're sure to find something valuable in this repository.

Awesome AI is a curated list of artificial intelligence resources including courses, tools, apps, and open-source projects. It covers a wide range of topics such as machine learning, deep learning, natural language processing, robotics, conversational interfaces, data science, and more. The repository serves as a comprehensive guide for individuals interested in exploring the field of artificial intelligence and its applications across various domains.

Start Machine Learning in 2024 is a comprehensive guide for beginners to advance in machine learning and artificial intelligence without any prior background. The guide covers various resources such as free online courses, articles, books, and practical tips to become an expert in the field. It emphasizes self-paced learning and provides recommendations for learning paths, including videos, podcasts, and online communities. The guide also includes information on building language models and applications, practicing through Kaggle competitions, and staying updated with the latest news and developments in AI. The goal is to empower individuals with the knowledge and resources to excel in machine learning and AI.

The Intel® AI Reference Models repository contains links to pre-trained models, sample scripts, best practices, and tutorials for popular open-source machine learning models optimized by Intel to run on Intel® Xeon® Scalable processors and Intel® Data Center GPUs. The purpose is to quickly replicate complete software environments showcasing the AI capabilities of Intel platforms. It includes optimizations for popular deep learning frameworks like TensorFlow and PyTorch, with additional plugins/extensions for improved performance. The repository is licensed under Apache License Version 2.0.

The Intel® AI Reference Models repository contains links to pre-trained models, sample scripts, best practices, and tutorials for popular open-source machine learning models optimized by Intel to run on Intel® Xeon® Scalable processors and Intel® Data Center GPUs. It aims to replicate the best-known performance of target model/dataset combinations in optimally-configured hardware environments. The repository will be deprecated upon the publication of v3.2.0 and will no longer be maintained or published.

Generative AI JS is a JavaScript library that provides tools for creating generative art and music using artificial intelligence techniques. It allows users to generate unique and creative content by leveraging machine learning models. The library includes functions for generating images, music, and text based on user input and preferences. With Generative AI JS, users can explore the intersection of art and technology, experiment with different creative processes, and create dynamic and interactive content for various applications.

Open AI is a powerful tool for artificial intelligence research and development. It provides a wide range of machine learning models and algorithms, making it easier for developers to create innovative AI applications. With Open AI, users can explore cutting-edge technologies such as natural language processing, computer vision, and reinforcement learning. The platform offers a user-friendly interface and comprehensive documentation to support users in building and deploying AI solutions. Whether you are a beginner or an experienced AI practitioner, Open AI offers the tools and resources you need to accelerate your AI projects and stay ahead in the rapidly evolving field of artificial intelligence.

DataHorse is an open-source tool and Python library that simplifies data science for everyone. It allows users to interact with data in plain English without requiring technical skills. Users can create graphs, modify data, and build machine learning models to make predictions. The tool is designed to help businesses and individuals quickly understand their data and make data-driven decisions with ease.

**RVC_CLI: Retrieval-based Voice Conversion Command Line Interface** This command-line interface (CLI) provides a comprehensive set of tools for voice conversion, enabling you to modify the pitch, timbre, and other characteristics of audio recordings. It leverages advanced machine learning models to achieve realistic and high-quality voice conversions. **Key Features:** * **Inference:** Convert the pitch and timbre of audio in real-time or process audio files in batch mode. * **TTS Inference:** Synthesize speech from text using a variety of voices and apply voice conversion techniques. * **Training:** Train custom voice conversion models to meet specific requirements. * **Model Management:** Extract, blend, and analyze models to fine-tune and optimize performance. * **Audio Analysis:** Inspect audio files to gain insights into their characteristics. * **API:** Integrate the CLI's functionality into your own applications or workflows. **Applications:** The RVC_CLI finds applications in various domains, including: * **Music Production:** Create unique vocal effects, harmonies, and backing vocals. * **Voiceovers:** Generate voiceovers with different accents, emotions, and styles. * **Audio Editing:** Enhance or modify audio recordings for podcasts, audiobooks, and other content. * **Research and Development:** Explore and advance the field of voice conversion technology. **For Jobs:** * Audio Engineer * Music Producer * Voiceover Artist * Audio Editor * Machine Learning Engineer **AI Keywords:** * Voice Conversion * Pitch Shifting * Timbre Modification * Machine Learning * Audio Processing **For Tasks:** * Convert Pitch * Change Timbre * Synthesize Speech * Train Model * Analyze Audio

Cog is an open-source tool that lets you package machine learning models in a standard, production-ready container. You can deploy your packaged model to your own infrastructure, or to Replicate.

Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API. * **Highly performant** : web layer and task coordination built with Rust 🦀, which offers blazing speed in addition to efficient CPU utilization powered by async I/O * **Ease of use** : user interface purely in Python 🐍, by which users can serve their models in an ML framework-agnostic manner using the same code as they do for offline testing * **Dynamic batching** : aggregate requests from different users for batched inference and distribute results back * **Pipelined stages** : spawn multiple processes for pipelined stages to handle CPU/GPU/IO mixed workloads * **Cloud friendly** : designed to run in the cloud, with the model warmup, graceful shutdown, and Prometheus monitoring metrics, easily managed by Kubernetes or any container orchestration systems * **Do one thing well** : focus on the online serving part, users can pay attention to the model optimization and business logic

FarmVibes.AI is a repository focused on developing multi-modal geospatial machine learning models for agriculture and sustainability. It enables users to fuse various geospatial and spatiotemporal datasets, such as satellite imagery, drone imagery, and weather data, to generate robust insights for agriculture-related problems. The repository provides fusion workflows, data preparation tools, model training notebooks, and an inference engine to facilitate the creation of geospatial models tailored for agriculture and farming. Users can interact with the tools via a local cluster, REST API, or a Python client, and the repository includes documentation and notebook examples to guide users in utilizing FarmVibes.AI for tasks like harvest date detection, climate impact estimation, micro climate prediction, and crop identification.

ClearML Serving is a command line utility for model deployment and orchestration, enabling model deployment including serving and preprocessing code to a Kubernetes cluster or custom container based solution. It supports machine learning models like Scikit Learn, XGBoost, LightGBM, and deep learning models like TensorFlow, PyTorch, ONNX. It provides a customizable RestAPI for serving, online model deployment, scalable solutions, multi-model per container, automatic deployment, canary A/B deployment, model monitoring, usage metric reporting, metric dashboard, and model performance metrics. ClearML Serving is modular, scalable, flexible, customizable, and open source.

Chat with MLX is an all-in-one Chat Playground using Apple MLX on Apple Silicon Macs. It provides privacy-enhanced AI for secure conversations with various models, easy integration of HuggingFace and MLX Compatible Open-Source Models, and comes with default models like Llama-3, Phi-3, Yi, Qwen, Mistral, Codestral, Mixtral, StableLM. The tool is designed for developers and researchers working with machine learning models on Apple Silicon.

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.

Bittensor is an internet-scale neural network that incentivizes computers to provide access to machine learning models in a decentralized and censorship-resistant manner. It operates through a token-based mechanism where miners host, train, and procure machine learning systems to fulfill verification problems defined by validators. The network rewards miners and validators for their contributions, ensuring continuous improvement in knowledge output. Bittensor allows anyone to participate, extract value, and govern the network without centralized control. It supports tasks such as generating text, audio, images, and extracting numerical representations.

Awesome MLSecOps is a curated list of open-source tools, resources, and tutorials for MLSecOps (Machine Learning Security Operations). It includes a wide range of security tools and libraries for protecting machine learning models against adversarial attacks, as well as resources for AI security, data anonymization, model security, and more. The repository aims to provide a comprehensive collection of tools and information to help users secure their machine learning systems and infrastructure.

This repository is a comprehensive guide for individuals looking to start and improve their skills in Large Language Models (LLMs) without an advanced background in the field. It provides free resources, online courses, books, articles, and practical tips to become an expert in machine learning. The guide covers topics such as terminology, transformers, prompting, retrieval augmented generation (RAG), and more. It also includes recommendations for podcasts, YouTube videos, and communities to stay updated with the latest news in AI and LLMs.

Kafka-ML is a framework designed to manage the pipeline of Tensorflow/Keras and PyTorch machine learning models on Kubernetes. It enables the design, training, and inference of ML models with datasets fed through Apache Kafka, connecting them directly to data streams like those from IoT devices. The Web UI allows easy definition of ML models without external libraries, catering to both experts and non-experts in ML/AI.

Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. It is designed to be easy to get started with and simple to use for Java developers. DJL provides a native Java development experience and allows users to integrate machine learning and deep learning models with their Java applications. The framework is deep learning engine agnostic, enabling users to switch engines at any point for optimal performance. DJL's ergonomic API interface guides users with best practices to accomplish deep learning tasks, such as running inference and training neural networks.

Start consing with the help of a Common Lisp expert

Beginner-friendly Python guide including practical projects

python 程序开发工程师

This GPT can read all Streamlit Documantation and helps you about Streamlit.

Friendly Azure guide for beginners

I'm here to make your Streamlit apps go viral!

Creates Reforge framework feature maps in tabular format.

Guiding users in DTL usage with tools and interactive demos

Provides optimized, commented Python code.

Your wise R Shiny sage, offering enlightened guidance.

Creative coding for generative art.

R coding expert minimizing errors in R programming

Your purpose is to create the pytorch code to train language models using pytorch

An assistant to help you understand transfer learning better

I create personalized code templates for your projects.

Concise NeRF guide, summarizing top papers and aiding research.

I assist with Langflow CustomComponents.

Expert in teaching Pydantic with clear, accessible explanations.

我是一个温和的老师，以最温和的语气解答我学生的一切问题，聪明的你提问吧，加微信simons2035获取python\numpy\pandas\matplotlib全套思维导图吧！

A dual-expert in Pydantic V1 and V2, adept at online research for V2.