卷积 神经 英语是什么意思 - 英语翻译

• convolutional neural
• convolution neural

在 中文 中使用 卷积 神经 的示例及其翻译为 英语

深度卷积神经网络也拥有类似的结构。
Deep convolution neural networks have a similar structure.

卷积神经网络非常擅长图像分类。
Convolution neural networks are very good at classifying images.

卷积神经.
Convolutional NN.

卷积神经网络.
The Convolutional Neural Network.

图6：文本卷积神经网络（Kim,2014）.
Figure 6: A convolutional neural network for text(Kim, 2014).

卷积神经网络受视觉系统的结构启发而产生。
Convolutional nets are inspired by the visual system's structure.

该系统使用称为卷积神经网络(CNN)的机器学习模型，通常用于图像识别。
The system uses a machine learning model called a convolutional neural network(CNN), commonly used for image recognition.

本文将介绍如何使用Keras+Tensorflow创建卷积神经网络，并对其进行训练，以使得车辆保持在两条白线之间。
This document walks through how to create a convolution neural network using Keras+Tensorflow and train it to keep a car between two white lines….

卷积神经网络(ConvNets或CNNs)是一类在图像识别和分类等领域非常有效的神经网络。
Convolutional Neural Networks(ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification.

卷积神经网络的内部数据表示不考虑简单和复杂对象之间的重要的空间层次结构。
Internal data representation of a convolutional neural network does not take into account important spatial hierarchies between simple and complex objects.

卷积神经网络（CNN）是一类神经网络，已被证明在图像识别和分类等领域非常有效。
Convolution neural networks are a category of Neural Networks that have proven to be very effective in areas such as image recognition and classification.

卷积神经网络是一种深度神经网络(DNN)，工程师最近开始将该技术用于各种识别任务。
Convolutional neural networks are a form of deep neural networks(DNNs) that engineers have recently begun using for various recognition tasks.

卷积神经网络允许计算机高效地、整体地处理图像，而不需要把图像拆分成几个部分。
Convolution neural network enables the computer to process images efficiently and comprehensively, and does not need to decompose images into multiple parts.

卷积神经网络使计算机能够高效、整体地处理图像□□，而不必将图像分割成多个部分。
Convolution neural network enables the computer to process images efficiently and comprehensively, and does not need to decompose images into multiple parts.

卷积神经网络--通常用于图像处理的机器学习模型--使用这些轨迹来分离特定个体的反射。
A convolutional neural network- a machine-learning model commonly used for image processing- uses those tracklets to separate reflections by certain individuals.

报道称，科学家利用卷积神经网络(CNN)系统，制造出这项研究使用的人工智能系统。
According to the report, scientists used the Convolutional Neural Network(CNN) system to create the artificial intelligence system used in this study.

多亏了卷积神经网络，计算机不仅能够分辨猫和狗之间的区别，还能够识别不同品种的狗。
Thanks to convolutional neural networks, not only can computers tell the difference between cats and dogs, they can even recognise different breeds of dogs.".

卷积神经网络赢得了德国交通标志识别竞赛，识别准确率为99.46％（人类最高准确率是99.22％）。
A convolutional neural network wins the German Traffic Sign Recognition competition with 99.46% accuracy(vs. humans at 99.22%).

卷积神经网络通常用于处理图像，而循环神经网络（RNN）用于处理语言。
While convolutional neural networks are typically used for processing images, recurrent neural networks(RNNs) are used for processing language.

它作用于卷积神经网络和/或复发性神经网络，也可以运行在两个CPU和GPU。
It works on convolutional neural networks and/or recurrent neural networks, and can also run on both CPU and GPU.

卷积神经网络IP内核：OpenVX*的扩展，它允许开发人员利用训练过的神经网络模型进行对象检测任务。
Convolutional Neural Network IP Kernels: an extension of OpenVX* which allows developers to leverage trained neural network models for their object detection tasks.

与卷积神经网络不同，胶囊神经网络可以识别相对空间关系，这在天气模式的演变中很重要。
Unlike convolutional neural networks, capsule neural networks can recognize relative spatial relationships, which are important in the evolution of weather patterns.

我们将为你展示如何训练和优化基础神经网络、卷积神经网络和长短期记忆神经网络。
We will show you how to train and optimize basic neural networks, convolutional neural networks, and long short-term memory networks.

深度学习是机器学习的一个领域，它使计算机能够接受培训，并通过卷积神经网络（CNN）等结构进行学习。
Deep learning is an area of machine learning that enables computers to be trained and learn through architectures such as convolutional neural networks(CNNs).

胶囊神经网络的另一个重要优点是，它们不需要卷积神经网络那么多的训练数据。
Another significant advantage of capsule neural networks was that they don't require as much training data as convolutional neural networks.

在这篇文章中，我们将总结计算机视觉和卷积神经网络领域的许多新的重要发展。
In this post, we will go into summarizing a lot of the new and important developments in the field of computer vision and convolutional neural networks.

然后，他们使用NVIDIAGPU和cuDNN加速的TensorFlow深度学习框架，训练卷积神经网络根据乳腺影像预测癌症诊断。
Then, using NVIDIA GPUs with the cuDNN-accelerated TensorFlow deep learning framework, they trained a convolutional neural network to predict cancer diagnoses based on breast imaging.

整个神经网络有一个损失函数，我们为神经网络开发的所有贴士和技巧，仍然适用于卷积神经网络。
The whole network has a loss function and all the tips and tricks that we developed for neural networks still apply on Convolutional Neural Networks.

注意力还能用于卷积神经网络和RNN之间的界面。
Attention can also be used on the interface between a convolutional neural network and an RNN.

这样做，该系统采用卷积神经网络（CNN），机器学习模型已经成为图像处理任务的重地。
To do so, the system uses a convolutional neural network(CNN), a machine-learning model that's become a powerhouse for image-processing tasks.