DEEP LEARNING FRAMEWORKS in Chinese translation

• 深度学习框架

Examples of using Deep learning frameworks in English and their translations into Chinese

Apple's deep learning frameworks are tuned for just one single purpose: pushing data as quickly as possible through the network's layers.
苹果的深度学习框架只是为了一个目的：通过网络层级尽可能快地传递数据。

In addition to general-purpose Deep Learning frameworks, we saw a large number of Reinforcement Learning frameworks being released, including.
除了通用的深度学习框架外，我们还看到大量的强化学习框架正在发布中，其中包括：.

Now you have tested two different deep learning frameworks, and you have seen how to switch between frameworks..
现在，您已测试了两种不同的深度学习框架，您已了解如何切换框架。

However, most popular deep learning frameworks scale across GPUs or learners within a server, but not to many servers with GPUs.
然而，大多数流行的深度学习框架都是在一个服务器上，而不是在许多服务器上使用GPU。

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.
在过去几年中，开源社区里对各种新冒出的深度学习框架有着许多的讨论。

Currently, all employees at Megvii train and deploy algorithms using Brain++, without relying on deep learning frameworks developed by third parties.
目前，旷视全员使用Brain++来训练、部署算法，无须依赖第三方开发的深度学习框架。

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.
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。

That said, if you are determined to understand how Deep Learning frameworks work, you might want to understand how automatic differentiation works here.
也就是说，如果你一定要弄清楚深度学习框架是怎样工作的，你可能想在下面这篇文章中理解自动求导是如何实现的：.

This paper presents a comparative study of four deep learning frameworks, namely Caffe, Neon, Theano, and Torch, on three aspects: extensibility, hardware utilization, and speed.
这篇论文给你提供了五个常用的深度学习框架的比较研究：TensorFlow，Torch，Caffe，Neon和Theano，主要包括三个重要的方面：硬件的利用率，可扩展性和训练速度。

Deep learning frameworks like TensorFlow, PyTorch, Caffe, MXNet, and Chainer have reduced the effort and skills needed to train and use deep learning models.
像TensorFlow、PyTorch、Caffe、MXNet和Chainer这样的深度学习框架减少了训练和使用深度学习模型所需的工作量和技能。

However, most popular deep learning frameworks scale across GPUs or learners within a server, but not to many servers with GPUs.
但是，现在大多数流行的深度学习框架可以在一个服务器内的GPU或学习者之间进行扩展，但多个服务器之间则无法进行扩展。

MXNet is an open-source, deep learning framework available from WEB.
MXNet是一个开源深度学习框架，可以从WEB.

We extend the deep learning framework to create learnable structural features.
我们扩展了深度学习框架以创造可学习的结构特征。

Moreover, Amazon has selected MXnet as its deep learning framework at AWS.
亚马逊选择MXNet作为AWS的深度学习框架。

PFN develops and provides Chainer, an open source deep learning framework.
PFN致力于开发并提供开源深度学习框架Chainer。

PyTorch- A deep learning framework that puts Python first.
PyTorch是一个深度学习框架，将Python放在首位。

BigDL It is a distributed deep learning framework for Apache Spark.
BigDL是一个用于ApacheSpark的分布式深度学习库。

We are standardizing OpenAI's deep learning framework on PyTorch.
OpenAI表示，其正在PyTorch上标准化OpenAI的深度学习框架。

Neon is Nervana's Python based Deep Learning framework.
Neon是Nervana公司的一个基于Python的深度学习库。

This is an open source deep learning framework that is currently an incubator project in the Apache Software Foundation.
这是一个开源深度学习框架，目前是Apache软件基金会n的孵化器项目。