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It is a Neural Net Training Interface on TensorFlow, with focus on speed + flexibility. It is a training interface based on TensorFlow, which means: you’ll use mostly tensorpack high-level APIs to do training, rather than TensorFlow low-level APIs.
技术使用统计数据和市场份额
您可以通过筛选地理位置、行业、公司规模、收入、技术使用情况、职位等来根据您的需求定制这些数据。您可以以Excel或CSV格式下载数据。
您可以获得有关此数据的提醒。您可以通过选择您感兴趣的技术来开始,然后当有新公司使用该技术时,您将会在您的收件箱中收到提醒。
您可以将这些数据导出到一个Excel文件,然后导入到您的CRM中。您也可以将这些数据导出到API。
Tensorpack 被用于 1 个国家
我们掌握了关于使用Tensorpack的2家公司数据。这个精心策划的名单可以下载,并附带了重要的公司具体信息,包括行业分类、组织规模、地理位置、融资轮次和收入数据等。
Technology
is any of
Tensorpack
公司 | 国家 | 行业 | 雇员 | 收入 | 技术 |
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德国 | Machinery Manufacturing | 4.2K | $86M | Tensorpack | |
Construction | 501 |
| Tensorpack |
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常见问题
我们的数据来自于从数百万家公司收集的招聘信息。我们在公司网站、招聘平台和其他招聘平台上监控这些招聘信息。分析招聘信息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。
我们每天更新数据,以确保您访问的是最新的可用信息。这一频繁的更新过程保证了我们的洞察力和情报反映了行业内的最新发展和趋势。
Tensorpack is a versatile and efficient Python library for building and training neural networks. It provides a high-level interface for working with deep learning models and offers a range of tools and utilities to streamline the development process. With Tensorpack, users can easily create complex neural network architectures, implement various training strategies, and optimize performance for different tasks in the field of machine learning.
Tensorpack falls under the category of Machine Learning Tools, specifically focusing on enhancing the training and deployment of neural networks. It offers a suite of functionalities aimed at simplifying the implementation of deep learning algorithms, such as data loading, model building, and distributed training support. By providing a comprehensive framework that integrates seamlessly with popular deep learning libraries like TensorFlow, Tensorpack empowers developers to efficiently leverage the full potential of neural networks for their applications.
Founded in 2017 by Yuxin Wu, Tensorpack emerged with the vision of addressing the complexities and challenges faced by developers in training deep learning models. Wu's motivation stemmed from the need to enhance the efficiency and scalability of neural network training pipelines, ultimately leading to the creation of TensorPack. Since its inception, TensorPack has gained traction in the machine learning community and has been adopted by a diverse range of users for various applications.
In terms of market share, TensorPack has established itself as a reliable and powerful tool within the machine learning landscape. Its robust feature set and ease of use have garnered a growing user base, indicating a positive trend in its market presence. With the increasing demand for efficient machine learning tools and the continuous advancements in neural network research, it is anticipated that TensorPack's market share will continue to expand in the future, maintaining its position as a prominent solution for building and training neural networks.
您可以访问 TheirStack.com,获取使用 Tensorpack 的公司更新名单。我们的平台提供了一个全面的数据库,涵盖了使用各种技术和内部工具的公司。
截至目前,我们拥有关于 2 家使用 Tensorpack 的公司的数据。
Tensorpack 被广泛应用于包括 "Machinery Manufacturing", "Construction" 在内的各个行业的各种组织中。欲了解所有使用 Tensorpack 的行业的完整列表,请访问 TheirStack.com。
一些使用Tensorpack的公司包括Linde Material Handling, Still以及更多公司。您可以在TheirStack.com上找到使用Tensorpack的2家公司完整列表。
根据我们的数据,Tensorpack 在 德国 (1 companies) 最受欢迎。然而,它被全世界的公司所使用。
您可以在TheirStack.com上搜索Tensorpack,来找到使用该技术的公司。我们跟踪数百万家公司的招聘信息,并借此发现他们正在使用的技术和内部工具。