DEEP LEARNING ARCHITECTURES 中文是什么意思 - 中文翻译

• 深度学习架构
• 的深度学习体系结构

在 英语 中使用 Deep learning architectures 的示例及其翻译为 中文

A brief survey of deep learning architectures is also included.
还包括了一个简短的深入学习架构的调查。

The RNN is one of the foundational network architectures from which other deep learning architectures are built.
RNN是一种基础网络架构，其他一些深度学习架构是基于它来构建的。

Advanced deep learning architectures data scientists should know!
数据科学家必须知道的10个深度学习架构！!

This book will allow you to get up to speed quickly using TensorFlow and to optimize different deep learning architectures.
本书将允许您快速使用TensorFlow快速加速，并优化不同的深度学习架构。

This section explores five of the deep learning architectures spanning the past 20 years.
本节将探讨过去20年来存在的深度学习架构中的5种。

You will also develop the mathematical understanding and intuition required to invent new deep learning architectures and solu….
您还将开发所需的数学理解和直觉，以便自己发明新的深度学习体系结构和解决方案。

They also proposed deep learning architectures that can manipulate structured data, such as graphs.
他们还提出了可以操作结构化数据的深度学习架构，如图形。

You will also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutions on your own.
您还将发展自己创造新的深度学习架构和解决方案所需的数学理解和直觉。

They also proposed deep learning architectures that can manipulate structured data, such as graphs.
他们还提出了可以操作结构数据（例如图数据）的深度学习架构。

For much faster, GPU-based implementations, as well as frameworks offering much more flexibility to build deep learning architectures, see Related Projects.
要快得多，基于GPU的实现以及为构建深度学习架构提供更多灵活性的框架，请参阅相关项目。

They also proposed deep learning architectures that can manipulate structured data, such as graphs.
他们还提出了可以处理结构化数据的深度学习架构，如「图」（graph）。

For much faster, GPU-based implementations, as well as frameworks offering much more flexibility to build deep learning architectures, see Related Projects.
为了更快，基于GPU的实现以及框架提供了构建深度学习体系结构的更多灵活性,请参考相关工程。

There are several Deep Learning architectures, that use different methods internally, to perform the same task.
有几种在内部使用不同方法的深度学习架构来实现相同的任务。

Region Based CNN architecture is said to be the most influential of all the deep learning architectures that have been applied to object detection problem.
基于区域的CNN架构据说是所有深度学习架构中对目标检测问题最有影响力的架构。

There are several Deep Learning architectures, that use different methods internally, to perform the same task.
有几种深度学习体系结构，它们在内部使用不同的方法来执行相同的任务。

For much faster, GPU-based implementations, as well as frameworks offering much more flexibility to build deep learning architectures, see Related Projects.
如果想要提高运行速度并使用基于GPU的实现以及为构建深度学习架构提供更多灵活性的框架，请参阅RelatedProjects。

Different deep learning architectures, such as CNNs and RNNs, support different types of applications(image, text, etc.).
不同的深度学习架构，例如CNN和RNN，支持不同类型的应用（图像，文本等）。

By using Ludwig, experts and researchers can simplify the prototyping process and streamline data processing so that they can focus on developing deep learning architectures.
通过使用Ludwig，专家和研究人员可以简化原型设计过程及数据处理，可以专注于开发深度学习架构。

Keras supports the major deep learning architectures, comes with a 30 second quick start guide, and has solid documentation.
Keras支持主流深度学习架构，自带30秒的快速入门指南，并有着完善的文档。

Deep learning architectures," they write,"in the form of deep convolutional and recurrent networks, can efficiently represent highly entangled quantum systems.".
深度学习架构，”他们写道，“以深度卷积和循环网络的形式，可以有效地代表高度纠缠的量子系统。

We explore how using relational inductive biases within deep learning architectures can facilitate learning about entities, relations, and rules for composing them.
我们探索在深度学习架构中使用关系归纳偏置如何有助于学习实体、关系以及构成它们的规则。

Deep Learning architectures like Sequence to Sequence are uniquely suited for generating text and researchers are hoping to make rapid progress in this area.
像序列到序列（SequencetoSequence）这样的深度学习架构是唯一可以适用于产生文本的，并且研究者希望在这个领域取得快速进步。

We explore how using relational inductive biases within deep learning architectures can facilitate learning about entities, relations, and rules for composing them.
我们探索在深度学习架构中如何使用关系归纳偏差可以促进对实体，关系和组成它们的规则的学习。

Implementing these deep learning architectures is certainly possible, but starting from scratch can be time-consuming, and they also need time to optimize and mature.
虽说我们完全有可能实现上述深度学习架构，但是从头开始可能很耗时，而且还有考虑优化和成熟的时间。

The last two decades gave us deep learning architectures, which greatly expanded the number and type of problems neural networks can address.
深度学习架构是最近20年内诞生的，它显著增加了神经网络可以解决的问题的数量和类型。

Deepnet implements some deep learning architectures and neural network algorithms, including BP, RBM, DBN, Deep autoencoder and so on.
Deepnet实现了一些深度学习架构和神经网络算法，包括BP、RBM、DBN、深度自编码器等等。

Implementing these deep learning architectures is certainly possible, but starting from scratch can be time-consuming, and they also need time to optimize and mature.
这些深度学习架构肯定是可以实现的，但从头开始可能很耗时，而且也需要时间来优化它们并让它们变得成熟。

IBM develops AI to speed deep learning architecture selection.
IBM开发AI以加速深度学习架构选择.

It uses a deep learning architecture that learns the meaning of the text, rather than associated words in a sentence.
其采用深度学习架构，能够学习文本含义，而不仅仅局限于句子中的关联词汇。