使用Chainer的公司
It is an open source deep learning framework written purely in Python on top of Numpy and CuPy Python libraries aiming at flexibility. It supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort.
使用Chainer的公司列表
curl --request POST \
--url "https://api.theirstack.com/v1/companies/search" \
--header "Accept: application/json" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer <api_key>" \
-d "{
\"company_technology_slug_or\": [
\"chainer\"
]
}"Technology
is any of
Chainer
员工数27kMotor Vehicle ManufacturingChainer中置信度
员工数7ConstructionChainer中置信度
员工数1kTransportation, Logistics, Supply Chain and StorageChainer中置信度
员工数16Events ServicesChainer中置信度
员工数68收入$3.2MMiningChainer中置信度
员工数76Transportation, Logistics, Supply Chain and StorageChainer中置信度
员工数332Entertainment ProvidersChainer中置信度- Technology, Information and InternetChainer中置信度
员工数91k收入$32BSoftware DevelopmentChainer低置信度
员工数20k收入$17BRenewable Energy Power GenerationChainer低置信度
我们拥有关于109家公司和用户使用Chainer的数据。我们的Chainer用户和客户列表可供下载，并附有重要的公司信息，包括行业分类、组织规模、地理位置、融资轮次和收入数据等。
技术使用统计和市场份额
如何定位 Chainer 用户
- 如何自定义此列表？
您可以根据地理位置、行业、公司规模、收入、技术使用、职位发布等需求自定义此数据。您可以将数据下载为Excel或CSV格式。
- 如何在公司采用此技术时收到通知？
您可以为此数据设置提醒。您可以通过选择您感兴趣的技术开始，当有新公司使用该技术时，您将收到收件箱中的提醒。
- 如何将此数据导入我的CRM？
您可以将此数据导出为Excel文件，然后导入到您的CRM中。您也可以将数据导出到API。
常见问题
我们的数据来源于从数百万家公司收集的职位发布。我们监控公司网站、招聘网站和其他招聘平台上的这些发布。分析职位发布提供了一种可靠的方法来了解公司正在使用的技术，包括他们使用的内部工具。
我们每天刷新数据，以确保您访问的是最新的信息。这一频繁的更新过程保证了我们的洞察和情报反映行业的最新发展和趋势。
Chainer is a deep learning framework that falls under the category of Machine Learning Tools. It is an open-source software library that provides a flexible, intuitive, and high-performance approach to deep learning. Developed using Python, Chainer allows users to define complex neural network models easily and efficiently. By providing a dynamic computational graph structure, Chainer enables developers to modify their networks on-the-fly during training, making it ideal for research and rapid prototyping in the field of deep learning.
Founded in 2015 by Preferred Networks, a Tokyo-based AI startup, Chainer was motivated by the need for a framework that could address the limitations of static computational graphs in other deep learning libraries. With a focus on flexibility and ease of use, Chainer gained popularity among researchers and developers who valued its dynamic approach to deep learning model construction. Over the years, Chainer has evolved to support various deep learning tasks, including computer vision, natural language processing, and more complex neural network architectures.
In terms of current market share, Chainer has carved out a niche for itself among deep learning enthusiasts and researchers. While giants like TensorFlow and PyTorch dominate the market, Chainer's unique approach to dynamic computation graphs continues to attract users looking for more flexibility in their deep learning models. However, with increasing competition and the rapid advancements in the deep learning landscape, Chainer's market share may face challenges in the future. As newer frameworks emerge with improved features and performance, the future growth of Chainer in the market remains uncertain.