在 中文 中使用 深度学习系统 的示例及其翻译为 英语
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但是现已有很多方法可以做到这一点，我们不一定要期望在深度学习系统中建立明确的历史记忆。
But there are many ways to do this, and we should not necessarily expect to find an explicit history memory inside a deep learning system.其他研究团队已经应用遗传或进化算法来优化深度学习系统。
Other research teams have applied genetic or evolutionary algorithms to the problem of optimizing deep-learning systems.大多数深度学习系统现在在『quasi』上做推理，它比用于训练的平台更小。
Most deep learning systems now do inferencing on the‘quasi' client side- on smaller platforms than used for training.事实上，他说：「深度学习系统变得越强大，它就越含糊。
Indeed, he said, the more powerful the deep-learning system becomes, the more opaque it can become.为了应对这些缺点，另一派研究人员开始倡导人工神经网络或连接人工智能，他们是当今深度学习系统的先驱。
In response to these shortcomings, rebel researchers began advocating for artificial neural networks, or connectionist AI, the precursors of today's deep-learning systems.深度学习系统根据人脑新皮质的神经网络建模，在那里出现了更高层次的认知。
Deep learning systems are modeled after the neural networks in the neocortex of the human brain, where higher-level cognition occurs.例如，犬类识别深度学习系统不明白狗狗通常有四条腿、皮毛和湿鼻子。
For example, a dog-spotting deep-learning system doesn't understand that dogs typically have four legs, fur, and a wet nose.通过比较众多用户的总体活动，深度学习系统甚至可以识别出可能会令用户感兴趣的全新项目。
By comparing the aggregate activity of numerous users, deep learning systems can even identify totally new items that might interest a user.第二，深度学习系统现在有着完善的可能性，因为三个原因：高配置CPU、更好的算法及更多可用的数据。
Deep learning systems are possible to implement now because of three reasons: High CPU power, Better Algorithms and the availability of more data.深度学习系统采用的方法与此类似，以至于这种系统一度被称为“神经网络”。
Deep learning systems take an analogous approach, so much so that they used to be called“neural nets.”.通过利用一些像TensorFlow等的开源框架，这些解决方案将大大降低创建复杂深度学习系统的工作量、时间以及成本。
Making use of open-source frameworks such as TensorFlow, these solutions will dramatically reduce the effort, time, and costs of creating complex deep learning systems.我们讨论无人驾驶汽车背后的技术，它们对基于规则决策引擎的依赖，以及大规模深度学习系统部署。
We talked about the technology behind self-driving vehicles, their reliance on rule-based decision engines, and deploying large-scale deep learning systems.我们不知如何证明我们正在建造系统的正确性，尤其是证明深度学习系统更加困难。
We don't know how to prove that the 快速数字化已经导致了大规模数据的产生，数据就是用于训练深度学习系统的氧气。
Rapid digitization has resulted in the production of large-scale data, and that data is oxygen for training deep learning systems.TensorFlow是一个由Google创建的开源软件库，用于实现机器学习和深度学习系统。
TensorFlow is an open source software library created by Google that is used to implement machine 我们得到了非常好的结果，“Ptucha说，他是深度学习系统和技术方面的专家。
We're getting very good results,” said Ptucha, who is an expert in deep learning systems and technologies.GPU充分利用了这种并行性，非常适合深度学习系统的定义，培训，优化和部署。
GPUs take full advantage 因为深度学习系统的精度会随着模型与数据集的变大而不断改善，所以我们会不断寻找最快的硬件。
Because the accuracy of deep learning systems improves as the models and datasets get larger, we always look for the fastest hardware we can find.深度学习系统的优点在于，与人类不同，它不会感到疲倦，并且可以持续评分。
The advantages of a deep learning system are that, unlike humans, it doesn't get tired and it can grade consistently….深度学习系统在ISO26262规范中的地位仍在探索之中。
The place for deep-learning systems within the requirements of ISO 26262 is still being defined.
