What is the translation of " ARTIFICIAL NEURAL NETWORKS " in Chinese?

• 人工神经网络

Examples of using Artificial neural networks in English and their translations into Chinese

SOLUTIONS MANUAL: Artificial Neural Networks by B.
Ann:人工神经网络（ArtificialNeuralNetworks）b….

Artificial neural networks are inspired by the human brain and the aims to study the connection between the neurons.
人工智能神经网络受到了人类大脑的启发，旨在研究神经元之间的连接。

As it turns out, something similar may be occurring when artificial neural networks are allowed to sleep and dream.
事实证明，当人工神经网络被允许睡觉和做梦时，类似的事情可能会发生。

Current artificial neural networks were based on 1950s understanding of how human brains process information.
当前的人工神经网络是基于二十世纪五十年代对人类大脑如何处理信息的理解。

Scientists at the University of California San Diego are coaching artificial neural networks to predict new stable materials.
现在，加州大学圣地亚哥分校的研究人员正在训练人工神经网络来预测新的稳定材料。

Modern artificial neural networks are composed of an array of software components, divided into inputs, hidden layers and outputs.
现代人工神经网络由一系列软件组成，分为输入、隐藏层和输出几个部分。

Now, researchers at the University of California San Diego are training artificial neural networks to predict new stable materials.
现在，加州大学圣地亚哥分校的研究人员正在训练人工神经网络来预测新的稳定材料。

Most artificial neural networks, such as feedforward neural networks, have no memory of the input they received just one moment ago.
大多数人造神经网络，如前馈神经网络，都没有记忆它们刚刚收到的输入。

These guys have shown that the approach biological systems use to learn, and to forget, can work with artificial neural networks too.
这些人已经表明，生物系统用来学习和遗忘的方法也可以与人工神经网络一起工作。

For this purpose, so-called artificial neural networks are used, mathematical models of the human brain.
为此目的，使用所谓的人工神经网络，即人脑的数学模型。

Deep learning is inspired by the ability of human brain neurons, multilayer artificial neural networks to learn, understand, and infer.
深度学习的灵感来自人脑神经元，多层人工神经网络进行学习，理解和推断的能力。

Qian and Cherry plan to develop artificial neural networks that can learn, forming"memories" from examples added to the test tube.
Qian和Cherry计划开发可以学习的人工神经网络，从添加至试管的例子中形成「记忆」。

In an interview with the BBC, Hinton said that over the years everyone felt that artificial neural networks were not worth mentioning.
辛顿在接受英国广播公司(BBC)采访时说，多年来大家都觉得人工神经网络不值一提。

Artificial intelligence uses artificial neural networks similar to the human brain and is able to recognize patterns and adapt to change.
人工智能使用类似于人脑的人工神经网络，能够识别模式并适应变化。

This kind of feedback is the basis of supervised learning, which includes large parts of pattern classification, artificial neural networks, and system identification.
这种反馈是监督学习的基础，其中包括大部分模式分类，人工神经网络和系统识别。

Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small difference between training and test performance.
尽管体积巨大，成功的深度人工神经网络在训练和测试性能之间可以展现出非常小的差异。

Artificial neural networks tend to simulate the human nervous system and brain functions, deriving its knowledge from physics, biology, and neuroscience.
人工神经网络倾向于模拟人类的神经系统和大脑功能，它的知识来源于物理、生物和神经科学。

Refinements in machine learning, inspired by neurobiology, have led to artificial neural networks that approach or, occasionally, surpass humans(1, 2).
在神经生物学的启发下，机器学习的改进导致人工神经网络接近或偶尔超越了人类（1,2）。

Artificial neural networks, which are computer systems modeled on the human brain and nervous system, and deep learning are also responsible for advances in AI.
人工神经网络是模仿人类大脑和神经系统的计算机系统，而深度学习也是AI进步的原因。

With its extensive range of libraries, you can build various applications in artificial neural networks, statistical data processing, image processing, and many others.
凭借其广泛的库，你可以在人工神经网络，统计数据处理，图像处理等中构建各种应用。

They built deep artificial neural networks that can accurately predict the neural responses produced by a biological brain to arbitrary visual stimuli.
他们建立了深层的人工神经网络，可以准确地预测生物大脑对任意视觉刺激产生的神经反应。

McCulloch and Pitts developed the first variants of what are now known as artificial neural networks, models of computation inspired by the structure of biological neural networks..
麦卡洛特和皮茨发展了我们今天称为人工神经网络的最早版本，来源于生物神经网络结构的计算模型。

Most artificial neural networks have two things in common: a huge number of weights, which are essentially the tunable parameters that networks learn during training;
大多数人工神经网络有两个共同点:大量的权值，本质上是网络在训练中学习的可调参数;

For example, biologically plausible deep/recurrent artificial neural networks are learning to solve pattern recognition tasks that seemed infeasible only 10 years ago.
例如，生物合理深/复发人工神经网络的学习来解决，只有10年前似乎是不可行的模式识别任务。

Researchers are trying to build artificial neural networks that can appropriately adjust to new information without abruptly forgetting what they learned before.
研究人员正试图建立人工神经网络，以便在突然忘记之前学过的东西的情况下，适当地调整新信息。

Gaston has turned to data analytics, specifically, artificial neural networks(ANN), a form of information processing inspired by biological systems such as the brain.
加斯顿已经转向数据分析，特别是人工神经网络（ANN），这是一种受大脑等生物系统启发的信息处理形式。

Researchers are trying to build artificial neural networks that can appropriately adjust to new information without abruptly forgetting what they learned before.
研究人员正试图建立人工神经网络，可以适当地适应新的信息，而不会突然忘记他们以前学过的东西。

They are also known as shift invariant or space invariant artificial neural networks(SIANN), based on their shared-weights architecture and translation invariance characteristics.
它们也被称为移位不变或空间不变人工神经网络（SIANN），基于它们的共享权重架构和平移不变性特征。

Algorithms based on artificial neural networks are not just reserved for the cloud, but can make smart decisions locally and enable new, revolutionary applications.
基于人工神经网络的算法不仅能支持云计算，还可在本地作出明智的决策，并实现新的、革命性的应用。

Day ago· Researchers built deep artificial neural networks that can accurately predict the neural responses produced by a biological brain to arbitrary visual stimuli.
他们建立了深层的人工神经网络，可以准确地预测生物大脑对任意视觉刺激产生的神经反应。