What surprises me is the the absence of Lua, although it is used in one of the major deep learning frameworks(Torch).
在过去的几年中,开源社区已经对所有新出现的深度学习框架讨论不断。
During the last few years, there have been lots of discussions among the open source community regarding all the new deep learning frameworks that emerged.
Caffe2是一个轻量级和模块化的深度学习框架,强调便携性,同时保持可扩展性和性能。
Caffe2 is a lightweight and modular deep learning framework that emphasizes portability while maintaining scalability and performance.
Apache MXNet(incubating) is a full-featured, highly scalable deep learning framework that supports creating and training state-of-the-art deep learning models.
Caffe, released in 2017, is known as a smaller machine learning framework for an artificial intelligence development company focusing on speed, modularity, and expressiveness.
Deep learning frameworks like TensorFlow, PyTorch, Caffe, MXNet, and Chainer have reduced the effort and skills needed to train and use deep learning models.
Today, we're open-sourcing the first production-ready release of Caffe2- a lightweight and modular deep learning framework emphasizing portability while maintaining scalability and performance.
AWS supports all the most important Machine Learning frameworks, together with TensorFlow, Caffe2, and Apache MXNet, so you will bring or develop any model you select.
During its 2018 Worldwide Developers Conference in June, Apple introduced an improved version of ML Core, its on-device machine learning framework for iOS.
Initially released in 2017, Caffe(Convolutional Architecture for Fast Feature Embedding) is a machine learning framework that focuses on expressiveness, speed, and modularity.
As those learning and training efforts mature across agencies, key lessons and common elements will be identified and pooled together to generate a wider common learning framework and principles.
Initially released in 2017, Caffe(Convolutional Architecture for Fast Feature Embedding) is a machine learning framework that focuses on expressiveness, speed, and modularity.
通过专业的学习框架、创新和技术,该联盟力求取得显着的学习成果。
Through expert learning frameworks, innovation, and technology, it focuses on achieving measurable learning outcomes.
This has its origins with Valiant(1984) who formulated the probably approximately correct, or PAC, learning framework.
在最近几年中,迁移学习作为一种新的学习框架被提出来用于解决这个问题。
In recent years, transfer learning has emerged as a new learning framework to address this problem.
第二,考虑到快速且泛化的数据收集,我们在基于社区的学习框架上试图支持集合性(而不是个人)语言。
Second, to allow for quick, generalizable data collection, we seek to support collective, rather than individual, languages, in a community-based learning framework.
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