Examples of using Neural network models in English and their translations into Chinese
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When getting started with the functional API, it is a good idea to see how some standard neural network models are defined.
Convolutional Neural Network IP Kernels: an extension of OpenVX* which allows developers to leverage trained neural network models for their object detection tasks.
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。Face++, MegaFace, FaceNet are other neural network models are designed specifically for facial recognition.
Face++、MegaFace、FaceNet是其他一些专门设计用来人脸识别的神经网络模型。Activity Rule: Most neural network models have short time-scale dynamics: local rules define how the activities of the neurons change in response to each other.
激励函数（ActivityRule）：大部分神经网络模型具有一个短时间尺度的动力学规则，来定义神经元如何根据其他神经元的活动来改变自己的激励值。I have already told you about Amazon Rekognition and described how it uses deep neural network models to analyze images by detecting objects, scenes, and faces.
我已告诉您有关AmazonRekognition的信息，并介绍了它如何使用深度神经网络模型通过检测对象、场景和面部来分析图像。Data scientists and developers will be able to utilise improved neural network models using Open Neural Network Exchange(ONNX) to increase AI adoption,” Climer wrote.
数据科学家和开发人员将能够利用改进的神经网络模型，以及使用开放式神经网络交换(ONNX)来增加人工智能的采用，”Climer写道。Neural network model.
神经网络模型.Deep learning takes the neural network model and updates it.
深度学习采用神经网络模型并对其进行更新。Batch Normalization is an effective method when training a neural network model.
BatchNormalization是训练神经网络模型的一种有效方法。Training the neural network model requires the following steps.
训练神经网络模型需要以下步骤.Android and Linux runtimes for neural network model execution.
Android和Linux运行环境，供执行神经网络模型.Continuous bag of word is a shallow neural network model.
B）连续性词包是浅层神经网络模型.(Right) Our neural network model which controls the window of attention.
控制注意力窗口的神经网络模型。Skip-gram is a deep neural network model.
C）Skip-gram是深度神经网络模型.The trouble is that if its neural networking models change, Google must build a new chip.
这样做的问题在于，如果神经网络模型发生改变，谷歌必须开发出一款新的芯片。The trouble is that if its neural networking models change, Google must build a new chip.
但问题是如果神经网络模型改变的话，谷歌必须要建立新的芯片。Recent neural networks models present both new opportunities and new challenges for developing conversational agents.
神经网络模型的最新进展为开发会话代理提供了新的机遇和挑战。An Autoencoder is a Neural Network model whose goal is to predict the input itself, typically through a“bottleneck” somewhere in the network. .
Autoencoder是一种神经网络模型，目标为通过网络中的一些“瓶颈”来预测网络的输入。However, the neural network model is getting larger and larger, which is expressed in the calculation of model parameters.
但是，神经网络模型也变得越来越大，这体现在模型参数的计算上。This neural network model achieved a higher accuracy in processing the challenging French Street Name Signs(FSNS) dataset.
该神经网络模型在对挑战性的FSNS（法国街道名称识别数据集，FrenchStreetNameSigns）数据集处理上，给出了更高的准.
