What is the translation of " 一个深度神经网络 " in English?

• a deep neural network

Examples of using 一个深度神经网络 in Chinese and their translations into English

一个深度神经网络具有两个以上的隐藏层。
A deep neural network has more than two hidden layers.

所以她把这项工作交给一个深度神经网络来处理。
So she's giving the job to a deep neural network.

研究员接下来使用一个深度神经网络（VGG-Face）创建特征。
The researchers then used a deep neural network(VGG-Face) to create features.

多伦多大学的AlexKrizhevsky创建了一个深度神经网络，能够从一百万个样本中自动学习识别图像。
Alex Krizhevsky of the University of Toronto created a deep neural network that automatically learned to recognize images from 1 million examples.

研究人员利用37236个头部CT图像，来训练一个深度神经网络，让其识别图像中是否包含关键或非关键的发现。
Researchers used 37,236 head CT scans to train a deep neural network to identify whether an image contained critical or non-critical findings.

一个深度神经网络可能有10到20个隐含层，而一个典型的神经网络可能只有几层。
A deep neural network might have 10 to 20 hidden layers, whereas a typical neural network may have only a few.

一家无人机公司最近也描述了一个深度神经网络，可以在复杂的真实环境中自动操作无人机。
A drone company recently described a deep neural network that autonomously flies drones in complex real-world environments.

研究人员利用37236个头部CT图像，来训练一个深度神经网络，让其识别图像中是否包含关键或非关键的发现。
Oermann and colleagues used 37,236 head CT scans to train a deep neural network to identify if an image contained critical findings.

AlphaGo通过训练一个深度神经网络来预测棋局位置的值，利用数百万场过去的比赛作为训练数据。
AlphaGo worked by training a deep neural network to predict the value of board positions, using millions of past games as training data.

研究人员利用37236个头部CT图像，来训练一个深度神经网络，让其识别图像中是否包含关键或非关键的发现。
Utilizing 37,236 head CT scans, researchers trained a deep neural network to identify if an image consisted of critical or non-critical findings.

本周，你将建立一个深度神经网络，你想要多少层就有多少层！!
This week, you will build a deep neural network, with as many layers as you want!

使用MATLAB、一个简单的网络摄像头和一个深度神经网络识别您周围的物体。
Use MATLAB, a simple webcam, and a deep neural network to identify objects in your surroundings.

图2：给定位置周围的区域会被栅格化（rasterized），然后被传递给一个深度神经网络。
Figure 2: The area surrounding a given location is rasterized and passed to a deep neural network.

使用MATLAB、一个简单的网络摄像头和一个深度神经网络识别您周围的物体。
See how to use MATLAB, a simple webcam, and a deep neural network to identify objects in your surroundings.

最后，但不是不重要，硬件需求对于运行一个深度神经网络模型是至关重要的。
Last but not the least, hardware requirements are essential for running a deep neural network model.

正如可以从论文的题目中推测出来的那样，涉及的回归模型是一个深度神经网络）。
(As you can probably infer from the title of the paper, the regression model in question was a deep neural network.).

正如你可能从本文的标题推断的，解决这个问题的回归模型是一个深度神经网络）.
(As you can probably infer from the title of the paper, the regression model in question was a deep neural network.).

但是，要进行学习，一个深度神经网络需要做的不仅仅是在各层神经网络中传递信息。
To learn, however, a deep neural net needed to do more than just send messages up through the layers in this fashion.

当然，在数学的角度上，你可以找出来哪一个深度神经网络节点被激活了。
Indeed, mathematically, you can find out which nodes of a deep neural network were activated.

一个深度神经网络在“学习”过数以千计的狗的照片后，能像人一样准确地识别出从未见过的照片中的狗。
After a deep neural network has“learned” from thousands of sample dog photos, it can identify dogs in new photos as accurately as people can.

不同的一点是，今年我们使用Deepnet，这是一个深度神经网络，而不是去年使用的集成模型（ensembles）。
For a change, this year we will use deepnets, BigML deep neural networks, instead of the ensembles that we used last year.

第一届ImageNet竞赛的获奖者是AlexKrizhevsky(NIPS2012)，他在YannLeCun开创的神经网络类型基础上，设计了一个深度卷积神经网络。
The winner of the first ImageNet competition Alex Krizhevsky(NIPS 2012) The deep convolutional neural network pioneered by Yann LeCun.

年，微软使用了一个深度学习神经网络进行语音识别。
In 2010, Microsoft used a deep learning neural network for speech recognition.

所以，解决方案是训练一个深度卷积神经网络（就像我们在第三章做的那样）。
The solution is to train a Deep Convolutional Neural Network(just like we did in Part 3).

一个简单的三层神经网络拥有一个隐藏层，而深度神经网络一词则表示其拥有多个隐藏层。
A simple three-layer neural net has one hidden layer while the term deep neural net implies multiple hidden layers.

而另一篇论文提出了一个快速训练深度神经网络的方法。
The other thesis raised a method of quickly training a deep neural network.

如今，我们可以分享更疯狂的功能--智能回复（SmartReply），一个使用深度神经网络训练的撰写email的功能。
Today we can share something even wilder- Smart Reply, a deep neural network that writes email.

我们的结果表明一个大型深度卷积神经网络在一个具有高度挑战性的数据集上使用纯有监督学习可以取得破纪录的结果。
The results show that a large, deep convolutional neural network is capable of achieving record-breaking results on a highly challenging dataset using purely supervised learning.

Keras是一个高级神经网络API，提供了一个Python深度学习库。
Keras is a high-level neural networks API and gives a Python deep learning library.