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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
aziende
Abbiamo dati su 50 aziende che usano TensorFlow.js. La nostra lista di clienti TensorFlow.js è disponibile per il download ed è arricchita con specifiche vitali dell'azienda, incluse classificazione industriale, dimensioni organizzative, posizione geografica, round di finanziamenti e cifre di ricavi, tra gli altri.
Azienda | Paese | Settore | Dipendenti | Entrate |
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![]() Stability AI | Regno Unito | Research Services | 174 | $1.2M |
Facet | Stati Uniti | Financial Services | 271 | $22M |
Doctolib | Francia | Software Development | 3.1K | |
Clearsale | Brasile | It Services And It Consulting | 2.6K | $50M |
VST | Regno Unito | Retail | 28 | |
Proximie | Regno Unito | Manufacturing | 150 | $13M |
![]() Stripe | Stati Uniti | Technology, Information And Internet | 9.5K | $7.4B |
Bitcoin.com | Saint Kitts e Nevis | Technology, Information And Internet | 180 | $8M |
Bosch Group | Stati Uniti | Manufacturing | 14K | $85B |
AVL | Austria | Motor Vehicle Manufacturing | 10K | $2.1B |
RingCentral | Stati Uniti | It Services And It Consulting | 6K | $2B |
Adobe | Stati Uniti | Software Development | 37K | $16B |
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TensorFlow.js è utilizzata in 10 paesi
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Aggiorniamo i nostri dati quotidianamente per garantire che tu abbia accesso alle informazioni più aggiornate disponibili. Questo processo di aggiornamento frequente garantisce che le nostre intuizioni e intelligenze riflettano gli ultimi sviluppi e tendenze all'interno dell'industria.
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.
Puoi accedere a un elenco aggiornato di aziende che utilizzano TensorFlow.js visitando TheirStack.com. La nostra piattaforma fornisce un database completo di aziende che utilizzano varie tecnologie e strumenti interni.
Fino ad ora, abbiamo dati su 50 aziende che utilizzano TensorFlow.js.
TensorFlow.js è utilizzato da una vasta gamma di organizzazioni in vari settori, inclusi "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". Per un elenco completo di tutti i settori che utilizzano TensorFlow.js, si prega di visitare TheirStack.com.
Alcune delle aziende che utilizzano TensorFlow.js includono Stability AI, Facet, Doctolib, Clearsale, VST, Proximie, Stripe, Bitcoin.com, Bosch Group, AVL e molte altre. Puoi trovare un elenco completo di 50 aziende che utilizzano TensorFlow.js su TheirStack.com.
Secondo i nostri dati, TensorFlow.js è più popolare in Stati Uniti (22 companies), Regno Unito (4 companies), Brasile (2 companies), Germania (2 companies), Austria (1 companies), Belgio (1 companies), Finlandia (1 companies), Francia (1 companies), India (1 companies), Saint Kitts e Nevis (1 companies). Tuttavia, è utilizzato da aziende in tutto il mondo.
Puoi trovare aziende che utilizzano TensorFlow.js cercandolo su TheirStack.com. Tracciamo le offerte di lavoro di milioni di aziende e le utilizziamo per scoprire quali tecnologie e strumenti interni stanno utilizzando.