Examples of using Convolutional neural networks in English and their translations into Chinese
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DeepFace is a facial recognition system based on deep convolutional neural networks created by a research group at Facebook in 2014.
DeepFace是一种基于2014年Facebook研究小组创建的深度卷积神经网络的面部识别系统。Perhaps the world's most exciting new technology today are deep neural networks, in particular convolutional neural networks, such as“Deep Learning.”.
如今，世界上最让人神往的黑科技大概就是深度神经网络了，尤其是卷积神经网络，例如“深度学习”。LeNet was one of the very first convolutional neural networks which helped propel the field of Deep Learning.
After seeing these neural networks, l think you have much better intuition about how to built effective convolutional neural networks.
了解了这些神经网络，我相信你会对如何构建有效的卷积神经网络更有感觉。In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks.
在这本书中，你从机器学习基础开始，然后继续学习神经网络，深度学习，然后是卷积神经网络。Deconvolutional networks(DN), also called inverse graphics networks(IGNs), are reversed convolutional neural networks.
解卷积网络（DN：Deconvolutionalnetworks），又称为逆图形网络（IGNs：inversegraphicsnetworks），是逆向的卷积神经网络。You can use convolutional neural networks(ConvNets, CNNs) and long short-term memory(LSTM) networks to perform classification and regression on image, time-series, and text data.
您可以使用卷积神经网络（ConvNet、CNN）和长短期记忆(LSTM)网络，对图像、时间序列和文本数据执行分类和回归。Some key enabler deep learning algorithms such as generative adversarial networks, convolutional neural networks, and model transfers have completely changed our perception of information processing.
一些关键的使能深度学习算法，如生成对抗网络，卷积神经网络和模型迁移，彻底改变了我们对信息处理的方法。To this end, we develop a multi-scale patch-based pattern extraction approach and combine it with convolutional neural networks to estimate building condition from visual clues.
为此，我们开发了一种多尺度基于patch模式的提取方法，并将其与卷积神经网络相结合，从视觉线索估计建筑物状况。I am currently training convolutional neural networks(convnets) with 7 or 8 layers in total, using much larger intermediate representations and many more parameters.
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。Classic convolutional neural networks(CNNs) and architectures such as AlexNet, VGGNet, GoogleNet, and ResNet, are widely used as the baseline model in facial recognition.
经典的卷积神经网络（CNN）和架构，如AlexNet、VGGNet、GoogleNet和ResNet，被广泛地用作人脸识别的基线模型。To do so, the system uses a convolutional neural network(CNN), a machine-learning model that's become a powerhouse for image-processing tasks.
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。The system uses a machine learning model called a convolutional neural network(CNN), commonly used for image recognition.
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。Convolutional neural network has strong characteristic extraction ability. It is widely used in areas such as image classification recognition, and target tracking.
卷积神经网络，拥有强大的特征提取能力，使其在图像分类识别、目标跟踪等领域运用广泛。The CNN LSTM architecture involves using Convolutional Neural Network(CNN) layers for feature extraction on input data combined with LSTMs to support sequence prediction.
CNNLSTM结构涉及在输入数据中使用卷积神经网络（CNN）层做特征提取并结合LSTM来支持序列预测。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。A convolutional neural network- a machine-learning model commonly used for image processing- uses those tracklets to separate reflections by certain individuals.
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。According to the report, scientists used the Convolutional Neural Network(CNN) system to create the artificial intelligence system used in this study.
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。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(CNN) can perform impressive feats of image recognition, but it is difficult to trace exactly how they arrive at their decisions.
卷积神经网络(CNN)可以执行令人印象深刻的图像识别功能，但是很难准确地跟踪它们如何做出决定。
