Examples of using 传统神经 in Chinese and their translations into English
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传统神经网络只包含2到3个隐藏层，而深度网络可能包含多达150个隐藏层。
A traditional neural network might consist of 2-3 hidden layers but a deep 假设你要求传统神经网络找出卫星照片中的物体。
Say you ask your conventional neural network to figure out which objects are what in satellite photos.传统神经网络只包含2到3个隐藏层，而深度网络可能包含多达150个隐藏层。
There are 2- 3 hidden layers in the traditional neural networks, whereas the deep networks may have as many as 150.传统神经网络只包含2到3个隐藏层，而深度网络可能包含多达150个隐藏层。
Conventional neural networks only contain 2-3 hidden layers, while deep networks can have 150.专注于让传统神经网络以量子速度进行训练是一个很自然的起点，无疑也是一项非常重要的工作。
Focusing on getting traditional neural networks to train at quantum speed is a natural starting point and unquestionably highly important work.Combinations with other parts of speech
传统神经网络只包含2到3个隐藏层，而深度网络可能包含多达150个隐藏层。
Traditional neural networks might only contain 2-3 hidden layers, while deep networks can have as many as 150.传统神经网络只包含2到3个隐藏层，而深度网络可能包含多达150个隐藏层。
The deep networkscan have around 150 hidden layers while traditional neural networks contain only 2 to 3 hidden layers.传统神经网络只包含2到3个隐藏层，而深度网络可能包含多达150个隐藏层。
According to a Mathwork blog, traditional neural networks only contain 2-3 hidden layers, while deep networks can have as many as 150.传统神经网络只包含2到3个隐藏层，而深度网络可能包含多达150个隐藏层。
A traditional neural network contains only 2-3 hidden layers while deep networks can contain as much as 150 hidden layers.以上这些优势解决了传统神经网络的大部分（如果不是全部的话）局限性问题。
These benefits solve most, if not all, of the limitations of traditional neural networks.传统神经网络只包含2到3个隐藏层，而深度网络可能包含多达150个隐藏层。
Traditional neural networks contain only two or three hidden layers, while deep 传统神经网络只包含2到3个隐藏层，而深度网络可能包含多达150个隐藏层。
Traditional neural networks only contain 2-3 hidden layers, while some recent deep networks have as many as 150.传统神经网络只包含2到3个隐藏层，而深度网络可能包含多达150个隐藏层。
Traditional neural networks only contain 2-3 hidden layers, while deep networks can have 100 or even more.尽管如此，很难训练神经网络，但是，受到约束时，会比传统神经网络更有效。
Despite this, the 在下篇博文中我将讨论贝叶斯神经网络（BayesianNeuralNetworks）以及她们是如何解决传统神经网络中存在的上述问题的。
In the next blog post I will discuss Bayesian 这些门分别标识为Z、H⊗N、X⊗N，模拟传统神经网络中的权向量。
These gates, denoted Z, H⊗N, and X⊗N,mimic the weight vectors in the traditional neural networks.本书向您展示了这些优雅模型的结构如何比传统神经网络更接近人类大脑的结构;
This book shows you how the structure of these elegant modelsis much closer to that of human brains than traditional neural networks;没有RN模块的传统神经网络大程度上落后，正确率约63%。
Traditional neural networks without the RN module faltered far behind, netting around 63 percent.跟传统神经网络一样，该架构是端到端可微的，可以被梯度下降算法训练。
Like conventional neural networks, the architecture is differentiable end-to-end and can be trained with gradient descent.传统神经网络只包含2到3个隐藏层，而深度网络可能包含多达150个隐藏层。
Traditional shallow neural networks contain about 2-3 hidden layers, while deep networks can have hundreds or more hidden layers.传统的神经网络使用单个网络来存储许多模式。
Traditional neural networks use a single network to store many patterns.然而，传统的神经网络并不能做到这些。
However, traditional artificial neural networks cannot maintain this persistence.与传统的神经网络架构有许多不同之处。
There are varied problems with traditional Neural Networks.在传统的神经网络中，我们假设所有的输入之间相互独立。
In a traditional neural network we assume that all inputs are independent of each other.它们很可能在2019年及以后取代许多传统的神经网络。
Capsule networks have already arrived-and they are likely to replace many conventional neural networks in 2019 and beyond.传统的神经科学家旨在找到将单个神经元简单化的方法。
多年来，传统神经科学认为人脑是一个独立的物体，而无视于人脑与人类所生活的社会环境之间的相互影响。
Traditional neuroscience has for many years considered the human brain as an isolated entity and ignored influences of and to the social environments in which humans live.传统的神经网络并不能做….
Traditional networking isn't working….学生进来我的课期待传统的神经或心理健康课程。
