Best AI tools for< Train A Machine Learning Model >

Full Stack AI is a tool that allows users to generate a full-stack Next.js app using an AI CLI. The app will be built with TypeScript, Tailwind, Prisma, Postgres, tRPC, authentication, Stripe, and Resend.

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.

Baseten is a machine learning infrastructure that provides a unified platform for data scientists and engineers to build, train, and deploy machine learning models. It offers a range of features to simplify the ML lifecycle, including data preparation, model training, and deployment. Baseten also provides a marketplace of pre-built models and components that can be used to accelerate the development of ML applications.

Weights & Biases is a machine learning platform that helps data scientists and engineers build, train, and deploy machine learning models. It provides a central location to track and manage all of your machine learning projects, and it offers a variety of tools to help you collaborate with others and share your work.

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.

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.

Artificial Intelligence: A Modern Approach, 4th US ed. is the authoritative, most-used AI textbook, adopted by over 1500 schools. It covers the entire spectrum of AI, from the fundamentals to the latest advances. The book is written in a clear and concise style, with a wealth of examples and exercises. It is suitable for both undergraduate and graduate students, as well as professionals in the field of AI.

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.

Lobe is a free and easy-to-use machine learning tool for Mac and PC that allows users to train machine learning models and deploy them to any platform of their choice. It provides a user-friendly interface for creating, training, and deploying machine learning models without requiring extensive coding knowledge.

Globose Technology Solutions Pvt Ltd (GTS) is an AI data collection company that provides various datasets such as image datasets, video datasets, text datasets, speech datasets, etc., to train machine learning models. They offer premium data collection services with a human touch, aiming to refine AI vision and propel AI forward. With over 25+ years of experience, they specialize in data management, annotation, and effective data collection techniques for AI/ML. The company focuses on unlocking high-quality data, understanding AI's transformative impact, and ensuring data accuracy as the backbone of reliable AI.

LAION is a non-profit organization that provides datasets, tools, and models to advance machine learning research. The organization's goal is to promote open public education and encourage the reuse of existing datasets and models to reduce the environmental impact of machine learning research.

BuildAi is an AI tool designed to provide the lowest cost GPU cloud for AI training on the market. The platform is powered with renewable energy, enabling companies to train AI models at a significantly reduced cost. BuildAi offers interruptible pricing, short term reserved capacity, and high uptime pricing options. The application focuses on optimizing infrastructure for training and fine-tuning machine learning models, not inference, and aims to decrease the impact of computing on the planet. With features like data transfer support, SSH access, and monitoring tools, BuildAi offers a comprehensive solution for ML teams.

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.

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.

Comet ML is a machine learning platform that integrates with your existing infrastructure and tools so you can manage, visualize, and optimize models—from training runs to production monitoring.

Comet ML is a machine learning platform that integrates with your existing infrastructure and tools so you can manage, visualize, and optimize models—from training runs to production monitoring.

ClearML is an open-source, end-to-end platform for continuous machine learning (ML). It provides a unified platform for data management, experiment tracking, model training, deployment, and monitoring. ClearML is designed to make it easy for teams to collaborate on ML projects and to ensure that models are deployed and maintained in a reliable and scalable way.

PredictModel is an AI tool that specializes in creating custom Machine Learning models tailored to meet unique requirements. The platform offers a comprehensive three-step process, including generating synthetic data, training ML models, and deploying them to AWS. PredictModel helps businesses streamline processes, improve customer segmentation, enhance client interaction, and boost overall business performance. The tool maximizes accuracy through customized synthetic data generation and saves time and money by providing expert ML engineers. With a focus on automated lead prioritization, fraud detection, cost optimization, and planning, PredictModel aims to stay ahead of the curve in the ML industry.

SentiSight.ai is a machine learning platform for image recognition solutions, offering services such as object detection, image segmentation, image classification, image similarity search, image annotation, computer vision consulting, and intelligent automation consulting. Users can access pre-trained models, background removal, NSFW detection, text recognition, and image recognition API. The platform provides tools for image labeling, project management, and training tutorials for various image recognition models. SentiSight.ai aims to streamline the image annotation process, empower users to build and train their own models, and deploy them for online or offline use.

Anyscale is a company that provides a scalable compute platform for AI and Python applications. Their platform includes a serverless API for serving and fine-tuning open LLMs, a private cloud solution for data privacy and governance, and an open source framework for training, batch, and real-time workloads. Anyscale's platform is used by companies such as OpenAI, Uber, and Spotify to power their AI workloads.

MATLAB-Simulink-Challenge-Project-Hub is a repository aimed at contributing to the progress of engineering and science by providing challenge projects with real industry relevance and societal impact. The repository offers a wide range of projects covering various technology trends such as Artificial Intelligence, Autonomous Vehicles, Big Data, Computer Vision, and Sustainability. Participants can gain practical skills with MATLAB and Simulink while making a significant contribution to science and engineering. The projects are designed to enhance expertise in areas like Sustainability and Renewable Energy, Control, Modeling and Simulation, Machine Learning, and Robotics. By participating in these projects, individuals can receive official recognition for their problem-solving skills from technology leaders at MathWorks and earn rewards upon project completion.

This repository contains a hand-curated list of great machine (deep) learning resources for Natural Language Processing (NLP) with a focus on Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), attention mechanism, Transformer architectures/networks, Chatbot, and transfer learning in NLP.

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.

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.

Netsaur is a powerful machine learning library for Deno, offering a lightweight and easy-to-use neural network solution. It is blazingly fast and efficient, providing a simple API for creating and training neural networks. Netsaur can run on both CPU and GPU, making it suitable for serverless environments. With Netsaur, users can quickly build and deploy machine learning models for various applications with minimal dependencies. This library is perfect for both beginners and experienced machine learning practitioners.

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.

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, ...

ChatGPT is a large language model that can generate human-like text, translate languages, write different kinds of creative content, and answer your questions in a conversational way. It is trained on a massive amount of text data, and it is able to understand and respond to a wide range of natural language prompts. Here are 5 jobs suitable for this tool, in lowercase letters: 1. content writer 2. chatbot assistant 3. language translator 4. creative writer 5. researcher

The Oracle Cloud Infrastructure Data Science and AI services Examples repository provides demos, tutorials, and code examples showcasing various features of the OCI Data Science service and AI services. It offers tools for data scientists to develop and deploy machine learning models efficiently, with features like Accelerated Data Science SDK, distributed training, batch processing, and machine learning pipelines. Whether you're a beginner or an experienced practitioner, OCI Data Science Services provide the resources needed to build, train, and deploy models easily.

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