What is the translation of " 传统的神经网络 " in English?

• traditional neural networks
• traditional neural network
• conventional neural networks

Examples of using 传统的神经网络 in Chinese and their translations into English

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.

传统的神经网络并不能做到这点，看起来也像是一种巨大的弊端。
Traditional neural networks can't do this, and it seems like a major shortcoming.

然而，传统的神经网络通常不能实现这种连续的任务学习。
However, traditional neural networks are typically incapable of such sequential task learning without forgetting.

传统的神经网络花费了大量的精力，不断更新这些权重的每一个。
Conventional neural networks spend a lot of energy to continuously update every single one of these weights.

目前还不清楚传统的神经网络如何利用它对电影中先前事件的推理来告知后来的事件。
It's unclear how a traditional neural network could use its reasoning about previous events in the film to inform later ones.

传统的神经网络做不到这一点，而且这似乎是一个主要缺点。
Traditional neural networks can't do this, and it seems like a major shortcoming.

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.

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.

传统的神经网络不能做到这一点，这似乎是一个明显的缺点。
Traditional neural networks can't do this, and it seems like a major shortcoming.

在传统的神经网络中，我们假设所有的输入（和输出）是相互独立的。
In a traditional Neural Network, all inputs(and outputs) are assumed to be independent of each other.

Alhanai指出，在传统的神经网络中，数据的所有特征都被提供给基于网络的算法进行分析。
Alhanai notes that, in traditional neural networks, all features about the data are provided to the algorithm at the base of the network..

传统的神经网络假设所有的输入(和输出)在时间或到达的顺序上相互独立。
A traditional neural network assumes that all inputs(and outputs) are independent of each other in time or order of arrival.

使用传统的神经网络，你给出一个输入，你得到一个输出。
Using traditional neural networks, you give an input and you get an output.

在传统的神经网络中，我们假设所有的输入(和输出)都是相互独立的。
In a traditional neural network we assume that all inputs(and outputs) are independent of each other.

然而，传统的神经网络通常不能进行这种连续的任务学习，这个缺点被称为灾难性遗忘。
However, traditional neural networks are typically incapable of such sequential task learning without forgetting.

传统的神经网络并不能如此，这似乎是一个主要的缺点。
Traditional neural networks can't do this, and it seems like a major shortcoming.

这个呢，就是我们所谓的深度神经网络，因为它比传统的神经网络有更多的中间层。
We call this a“deep neural network” because it has more layers than a traditional neural network.

如果使用传统的神经网络，你给出一个输入，得到一个输出。
Using traditional neural networks, you give an input and you get an output.

但是传统的神经网络就不能做到这一点，这似乎是一个很大的缺陷。
Traditional neural networks can't do this, and it seems like a major shortcoming.

传统的神经网络在图像分类中有较多的参数，如果在CPU上进行训练，会花费大量的时间。
Traditional neural networks that are very good at doing image classification have many more paramters and take a lot of time if trained on CPU.

就像传统的神经网络一样，在单个单元内编码一个分布式输入可不是一件容易的事情。
As with conventional neural nets, it is not so easy to code a distributed input within a single cell.

使用传统的神经网络，你给出一个输入，你得到一个输出。
With the traditional neural networks, you give an input, you get an output.

在传统的神经网络中，我们假设所有的输入(和输出)都是相互独立的。
In a typical neural network, we go with an assumption that all inputs(and outputs) are independent of each other.

如果使用传统的神经网络，你给出一个输入，得到一个输出。
With the traditional neural networks, you give an input, you get an output.

有趣的是，我们并没有这种类型的卷积神经网络或者Q网络（Q-Network）（应用于强化学习）或者传统的神经网络的记忆。
It's interesting to note that we don't have this type of memory with CNNs or with Q-networks(for reinforcement learning) or with traditional neural nets.

传统的神经网络并不能做….
Traditional networking isn't working….

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.

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.

目前还不清楚传统的神经网络如何利用它对电影中先前事件的推理来告知后来的事件。
It is unclear how a traditional artificial neural network could use its reasoning about previous events in the film to inform later ones.