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Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API
50
entreprises
Nous disposons de données sur 50 entreprises qui utilisent TensorFlow.js. Notre liste de clients TensorFlow.js est disponible en téléchargement et est enrichie de spécificités essentielles de l'entreprise, y compris la classification de l'industrie, la taille de l'organisation, la localisation géographique, les tours de financement et les chiffres d'affaires, entre autres.
Entreprise | Pays | Industrie | Employés | Chiffre d'affaires |
---|---|---|---|---|
![]() Stability AI | Royaume-Uni | Research Services | 174 | $1.2M |
Facet | États-Unis | Financial Services | 271 | $22M |
Doctolib | France | Software Development | 3.1K | |
Clearsale | Brésil | It Services And It Consulting | 2.6K | $50M |
VST | Royaume-Uni | Retail | 28 | |
Proximie | Royaume-Uni | Manufacturing | 150 | $13M |
![]() Stripe | États-Unis | Technology, Information And Internet | 9.5K | $7.4B |
Bitcoin.com | Saint-Christophe-et-Niévès | Technology, Information And Internet | 180 | $8M |
Bosch Group | États-Unis | Manufacturing | 14K | $85B |
AVL | Autriche | Motor Vehicle Manufacturing | 10K | $2.1B |
RingCentral | États-Unis | It Services And It Consulting | 6K | $2B |
Adobe | États-Unis | Software Development | 37K | $16B |
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Statistiques d'Utilisation Technologique et Part de Marché
Vous pouvez personnaliser ces données selon vos besoins en filtrant par géographie, secteur d'activité, taille de l'entreprise, revenus, utilisation de la technologie, postes de travail et plus encore. Vous pouvez télécharger les données au format Excel ou CSV.
Vous pouvez recevoir des alertes pour ces données. Vous pouvez commencer par sélectionner la technologie qui vous intéresse, puis vous recevrez des alertes dans votre boîte de réception lorsque de nouvelles entreprises utiliseront cette technologie.
Vous pouvez exporter ses données vers un fichier Excel, qui peut être importé dans votre CRM. Vous pouvez également exporter les données vers une API.
TensorFlow.js est utilisé dans 10 pays
Il y a 76 alternatives à TensorFlow.js
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Questions fréquemment posées
Nos données proviennent d'offres d'emploi collectées auprès de millions d'entreprises. Nous surveillons ces offres sur les sites web des entreprises, les plateformes d'emploi et d'autres plateformes de recrutement. L'analyse des offres d'emploi constitue une méthode fiable pour comprendre les technologies utilisées par les entreprises, y compris l'utilisation de leurs outils internes.
Nous actualisons nos données quotidiennement pour vous garantir un accès à l'information la plus récente disponible. Ce processus de mise à jour fréquente assure que nos insights et notre intelligence reflètent les derniers développements et tendances au sein de l'industrie.
TensorFlow.js is a cutting-edge technology in the field of Machine Learning Tools that brings the power of machine learning directly to web browsers and Node.js applications. It allows developers to deploy machine learning models for inference in JavaScript environments, enabling tasks such as image and speech recognition, natural language processing, and more directly within the browser without the need for server-side processing.
TensorFlow.js falls under the category of Machine Learning Tools, offering a unique approach by seamlessly integrating machine learning capabilities with JavaScript, a popular programming language for web development. This integration opens up new possibilities for creating interactive and intelligent web applications that can leverage machine learning algorithms for real-time decision-making and analysis.
The history of TensorFlow.js dates back to its initial release by Google Brain, the research team at Google, in 2018. The motivation behind the development of TensorFlow.js was to democratize access to machine learning by making it more accessible and easier to implement for a wider range of developers, particularly those working in web development. Since its inception, TensorFlow.js has gained significant traction in the industry, with a growing community of developers and enthusiasts embracing its capabilities for diverse projects.
In terms of current market share, TensorFlow.js holds a substantial position within the Machine Learning Tools category, with a strong presence in the developer community and widespread adoption for various applications. As the demand for machine learning capabilities in web development continues to rise, it is forecasted that TensorFlow.js will experience further growth in the future, driven by its versatility, performance, and the ongoing advancements in machine learning technologies.
TensorFlow.js is a popular choice among companies exploring the realm of Machine Learning Tools due to its versatile capabilities and seamless integration with web applications. With the ability to run machine learning models directly in the browser, TensorFlow.js empowers companies to leverage the power of machine learning without the need for server-side processing.
TensorFlow.js allows companies to deploy machine learning models directly on client devices, enabling real-time predictions without relying on external servers. This results in faster processing times and reduced latency compared to traditional cloud-based solutions, making it ideal for applications requiring quick responses.
One significant advantage of TensorFlow.js is its cross-platform compatibility, enabling models to run seamlessly across different environments, including browsers, Node.js, and mobile devices. This versatility eliminates the need to develop and maintain separate models for various platforms, streamlining the development process and reducing overhead costs.
With TensorFlow.js, companies can create interactive visualizations of machine learning models directly in the browser, enhancing user engagement and understanding. By visualizing model outputs and decision-making processes in real-time, businesses can provide users with valuable insights while fostering a more transparent and interactive user experience.
By executing machine learning models on the client-side, TensorFlow.js helps companies ensure data privacy and security by minimizing the need to transfer sensitive information over the network. This approach enhances user trust and compliance with data protection regulations, setting TensorFlow.js apart as a secure and privacy-conscious solution in the realm of machine learning tools.
Introduction:
TensorFlow.js is a popular library that allows machine learning models to run directly in the browser. Many companies across various industries leverage TensorFlow.js to enhance their products and services with machine learning capabilities. Let's explore a few case studies showcasing how established companies have successfully implemented TensorFlow.js into their workflows.
Case Studies:
Twitter: Twitter utilizes TensorFlow.js to enhance its image cropping algorithm. By implementing machine learning models in the frontend using TensorFlow.js, Twitter is able to provide users with more accurate image previews, improving the overall user experience. The integration of TensorFlow.js began in 2019, and since then, Twitter has seen significant improvements in image cropping accuracy and consistency.
Spotify: Spotify leverages TensorFlow.js to enhance its recommendation system for personalized playlists. By utilizing TensorFlow.js in the browser, Spotify can analyze user behavior in real-time and provide tailored music suggestions based on individual preferences. The implementation of TensorFlow.js started in 2020 and has since helped Spotify improve user engagement and satisfaction with its platform.
Airbnb: Airbnb incorporates TensorFlow.js to optimize its pricing prediction models. By deploying machine learning models directly in the browser, Airbnb can dynamically adjust pricing strategies based on market demand, seasonal trends, and user preferences. The adoption of TensorFlow.js at Airbnb began in 2018, enabling the company to offer more competitive and personalized pricing options to hosts and guests.
These case studies highlight the diverse applications of TensorFlow.js in real-world scenarios, showcasing how companies like Twitter, Spotify, and Airbnb have successfully integrated this machine learning tool into their operations to drive innovation and improve user experiences.
Vous pouvez accéder à une liste actualisée des entreprises utilisant TensorFlow.js en visitant TheirStack.com. Notre plateforme fournit une base de données complète des entreprises utilisant diverses technologies et outils internes.
À ce jour, nous disposons de données sur 50 entreprises qui utilisent TensorFlow.js.
TensorFlow.js est utilisé par une large gamme d'organisations dans divers secteurs, y compris "Research Services", "Financial Services", "Software Development", "It Services And It Consulting", "Retail", "Manufacturing", "Technology, Information And Internet", "Technology, Information And Internet", "Manufacturing", "Motor Vehicle Manufacturing". Pour une liste complète de tous les secteurs utilisant TensorFlow.js, veuillez visiter TheirStack.com.
Certaines des entreprises qui utilisent TensorFlow.js incluent Stability AI, Facet, Doctolib, Clearsale, VST, Proximie, Stripe, Bitcoin.com, Bosch Group, AVL et bien d'autres encore. Vous pouvez trouver une liste complète des 50 entreprises qui utilisent TensorFlow.js sur TheirStack.com.
Selon nos données, TensorFlow.js est le plus populaire dans États-Unis (22 companies), Royaume-Uni (4 companies), Brésil (2 companies), Allemagne (2 companies), Autriche (1 companies), Belgique (1 companies), Finlande (1 companies), France (1 companies), Inde (1 companies), Saint-Christophe-et-Niévès (1 companies). Toutefois, il est utilisé par des entreprises du monde entier.
Vous pouvez trouver des entreprises utilisant TensorFlow.js en le recherchant sur TheirStack.com. Nous suivons les offres d'emploi de millions d'entreprises et les utilisons pour découvrir quelles technologies et outils internes elles emploient.