What is the translation of " 递归神经网络 " in English?

• recurrent neural network
• recurrent neural networks
• recursive neural networks

Examples of using 递归神经网络 in Chinese and their translations into English

递归神经网络RNN和CNN都将语言视为一个序列。
Recursive neural networks RNNs and CNNs both treat the language as a sequence.

递归神经网络.
A Recurrent Neural Network.

递归神经网络有两个输入，现在和最近的过去。
A Recurrent Neural Network has two inputs, the present and the recent past.

递归神经网络（RNN），负责前瞻性思维。
A recurrent neural network(RNN) responsible for forward thinking.

递归神经网络.
A Recursive Neural Network.

这些网络被称为递归神经网络，它们能反向馈送到前几层或它们的层中的后续节点。
These networks are called recurrent neural networks, and they can feed backwards to prior layers or to subsequent nodes within their layer.

B）允许在递归神经网络进行batching，用于处理序列数据，例如文本和事件序列。
(b) enable batching within recursive neural networks, used to process sequence data e.g. text and event sequences.

大型递归神经网络被用于学习输入字符串序列中的项之间的关系，然后生成文本。
Large recurrent neural networks are used to learn the relationship between items in the sequences of input strings and then generate text.

递归神经网络--用于处理具有序列特征的数据，如文本和股票价格。
Recurrent neural network- created to process data with sequence feature, such as text and stock prices.

最后，您将学习递归神经网络，它将帮助我们解决情感分析中的否定问题。
Lastly, you will learn about recursive neural networks, which finally help us solve the problem of negation in sentiment analysis.

Google利用递归神经网络（RNN）来帮助图像识别和语言翻译。
Google uses Recurrent Neural Networks(RNNs) to facilitate image recognition and language translation.

比方说，找出某个递归神经网络在产品里面是如何实现的。
For example, find out how a recurrent neural network is implemented in the product.

这种链状性质揭示了递归神经网络与序列和列表密切相关。
This chain-like nature reveals that recurrent neural networks are intimately related to sequences and lists.

就像RNN一样，递归神经网络可以使用向后传播（backpropagation）进行端到端的训练。
Just like RNNs, Recursive Neural Networks can be trained end-to-end using backpropagation.

递归神经网络可以被认为是同一网络的多个副本，每一步都传递一个消息给下一步。
A recurrent neural network can be thought of as multiple copies of the same network, each passing a message to a successor.

这样做需要消化卷积神经网络(CNNs)、递归神经网络(RNNs)、监督学习、无监督学习等概念。
Doing so requires digesting concepts like convolutional neural networks(CNNs), recurrent neural networks(RNNs), supervised learning, unsupervised learning, and so on.

递归神经网络的应用可以在语音(TTS)转换模型中找到。
The application of Recurrent Neural Networks can be found in text to speech(TTS) conversion models.

由递归神经网络(RNN)生成的字幕，在这种情况下，RNN被训练以识别字幕中的高级图像呈现。
Captions generated by a recurrent neural network(RNN), the RNN in this case is trained to identify high-level image presentations into captions.

在递归神经网络中，神经元可以直接或间接地通过下一层来影响自己。
In recurrent neural networks, neurons can influence themselves, either directly, or indirectly through the next layer.

对于递归神经网络，一个信号可能会在一个层中传播不止一次，所以上限深度可能是无限的。!
For a recurrent neural network, a signal might propagate through a layer more than once, so the CAP depth is potentially unlimited!

递归神经网络用来生成文本已经有一段时间了，但是最近才在深度学习领域取得进展，改进得足以用来生成图像。
Recurrent neural networks have been generating text for some time but only recently have advances in deep learning improved enough for images to be generated.

使用递归神经网络模型的深度学习可以帮助解决困扰了医学专业人员几十年的棘手的预测问题。
Deep learning, using recurrent neural network models, can aid in solving difficult prediction problems that have puzzled medical professionals for decades.

对Boltzmann机的一个解释是这样的：Hopfield递归神经网络的蒙特卡洛版。
One interpretation of the Boltzmann machine is that it is a Monte Carlo version of the Hopfield recurrent neural network.

这种类似链的性质表明，递归神经网络与序列和列表密切相关。
This chain-like nature reveals that recurrent neural networks are intimately related to sequences and lists.

对于递归神经网络，一个信号可能会在一个层中传播不止一次，所以上限深度可能是无限的。
For recurrent neural networks, in which a signal may propagate through a layer more than once, the CAP depth is potentially unlimited.

下面的案例研究利用深度学习来解决这个问题，特别是卷积递归神经网络和MEL频率提取。
The case study mentioned below uses deep learning to solve the problem, specifically convolution recurrent neural network along with Mel Frequency Extraction.

这些网络称为递归神经网络，可以向后反馈到前些层或其所在层中的后续节点。
These networks are called recurrent neural networks, and they can feed backwards to prior layers or to subsequent nodes within their layer.

递归神经网络可以通过一步一步地处理真实数据序列来预测序列生成，并预测接下来会发生什么。
Recurrent neural networks can be trained for sequence generation by processing real data sequences one step at a time and predicting what comes next.

我们都知道，递归神经网络在处理这类远程依赖性时会遇到问题。
Recurrent Neural Networks are known to have problems dealing with such long-range dependencies.

受机器学习组合技术的启发，这一系统使用了一系列卷积和递归神经网络。
Inspired by machine learning ensemble techniques, the system uses a range of convolutional and recurrent neural networks.