Examples of using 的卷积神经网络 in Chinese and their translations into English
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您可以使用MetalPerformanceShaders的卷积神经网络、NPSCNN或者Accelerate的基础神经网络子例程(subroutine)。
You can use the Metal Performance Shaders convolutional neural net, NPSCNN, or Accelerate basic 它是一个大型的卷积神经网络，由K.Simonyan和A.
LeNet：第一个成功的卷积神经网络应用，是YannLeCun在上世纪90年代实现的。
The first successful applications of Convolutional Networks were developed by Yann LeCun in 1990s.勒坤表示，目前使用最广泛的卷积神经网络几乎完全依赖于监督学习。
Today's most widely used convolutional neural nets rely almost exclusively on supervised learning.在我看来，一个简单的卷积神经网络(CNN)就是一系列神经网络层。
In my point of view a simple Convolutional Neural Net(CNN) is a sequence of layers.了解和使用最先进的卷积神经网络，如VGG、ResNet和Inception.
Understand and use state-of-the-art convolutional neural nets such as VGG, ResNet and Inception.ZFNet(2013)-ILSVRC2013的获胜者是来自MatthewZeiler和RobFergus的卷积神经网络。
ZF Net(2013)- The ILSVRC 2013 winner was a Convolutional Network from Matthew Zeiler and Rob Fergus.Watson将所有这些信息都融合到具有数万标签的卷积神经网络。
Watson soaked up all that information into a convolutional neural network with tens of thousands of tags in its vocabulary.GoogLeNet(2014)-ILSVRC2014的获胜者是来自于Google的Szegedy等人的卷积神经网络。
GoogLeNet(2014)- The ILSVRC 2014 winner was a Convolutional Network from Szegedy et al. from Google.是否有些问题是深度学习或者你使用的卷积神经网络所无法解决的？?
Are there problems that you think deep learning or the image-sensing convolutional neural nets you use can't solve?去年，苹果发布了MetalCNN和BNNS框架，用于创建基本的卷积神经网络。
Last year Apple announced the Metal CNN and BNNS frameworks for creating basic convolutional networks.在这种情况下，ECP5FPGA被用于在边缘设备上实现一个面部识别应用的卷积神经网络(CNN)加速器。
In this case an ECP5 FPGA was used to implement a Convolutional Neural Net(CNN) accelerator for a facial recognition application on the edge.最成功的深度学习网络是由YannLeCun开发的卷积神经网络（CNN）。
The most successful deep learning 为了避免这类问题，Pinscreen「建立了一个深层的卷积神经网络，可以不受约束的学习如何分割面部区域」。
To avoid that problem, Pinscreen“built a deep convolutional neural network that can learn how to segment a face region in a fully unconstrained image.”.研究人员随后使用Tensorflow来教授一个预先训练好的卷积神经网络（CNN），以识别AUV图像中发现的各种深海形态物种的个体。
Then by using Google's Tensorflow, scientists taught a pre-trained Convolutional Neural Network(CNN) to identify individuals of various deep-sea morphospecies found in the AUV images.现在我正在训练总数达7层或8层的卷积神经网络（convnets），使用了大得多的中间表征和更多的参数。
I am currently training convolutional neural networks(convnets) with 7 or 8 layers in total, using much larger intermediate representations and many more parameters.这迫使你的卷积神经网络看到所有角落。
This forces your convolutional neural network to look everywhere.现代的卷积神经网络是LeCun职业生涯的巅峰之作。
The modern convolutional neural network is a culmination of work throughout LeCun's career.即使是最简单的卷积神经网络也能更好地识别物体。
Even the simplest convolutional neural network recognizes objects better.我们已经讨论了LeNet，它是最早的卷积神经网络之一。
We discussed the LeNet above which was one of the very first convolutional neural networks.
