This book will teach you many of the core concepts behind neural networks and deep learning.
这本书将告诉你许多神经网络与深度学习后面的核心概念。
“Will you tell us a story?” said the queen, “one that is instructive and full of deep learning.”
“请您讲一个故事好吗？”王后说，“讲一个高深而富有教育意义的故事。”
IF YOU know what deep learning and functional skills are, then you are already on the way to understanding eduspeak.
如果你知道深度学习和功能性技能是什么，那你就离理解“教育话“不远了。
After working through the book you will have written code that USES neural networks and deep learning to solve complex pattern recognition problems.
在完成本书的学习后，你将可以编写代码来使用神经网络和深度学习来解决复杂的模式识别问题。
This approach (which Geoffrey Hinton of the University of Toronto, a doyen of the field, has dubbed "deep learning") need not be confined to computer-vision.
这种方法被这个领域内的资深科学家，多伦多大学的GeoffreyHinton称为“深度学习”(见注释5)，它不必仅限于处理计算机视觉问题。
The idea of learning the right representation for the data provides one perspective on deep learning.
学习数据正确的特征表达是看待深度学习的一种观点。
A framework for deep learning for dynamic online discussion in distance education is illustrated in...
本文阐述了在远程教育中进行面向深层学习的动态在线讨论的一个框架。
Another perspective on deep learning is that it allows the computer to learn a multi-step computer program.
关于深度学习另一种观点就是使得计算机能够学习一个多步的计算机程序。
It seems like every time deep learning is applied to a task, it produces the best-ever results for that task.
似乎每一次将深度学习应用到一项任务当中，都会产生最佳的结果。
A framework for deep learning for dynamic online discussion in distance education is illustrated in this paper.
本文阐述了在远程教育中进行面向深层学习的动态在线讨论的一个框架。
And you will have a foundation to use neural networks and deep learning to attack problems of your own devising.
并且你将拥有使用神经网络和深度学习来解决你自己发现的问题的基础。
The big limitation of deep learning is that almost all the value it's creating is in these input-output mappings.
深度学习所面临的一个重要限制是，其创造的几乎所有价值都在输入-输出映射当中。
The quintessential example of a deep learning model is the feedforward deepnetwork or multilayer perceptron (MLP).
深度学习模型的一个典型例子是前馈深度网络，或者说多层感知器（MLP）。
It will use computer vision, sensors and deep learning algorithms to keep track of what customers are picking up off the shelves.
系统将会使用机械视觉、感应器和深度学习算法来跟踪记录顾客从货架上选取的商品。
The purpose of this book is to help you master the core concepts of neural networks, including modern techniques for deep learning.
这本书的目标是帮助你掌握神经网络的核心概念，包括深度学习的前沿技术。
In taking on the more intractable challenges, Google is looking to draw on deep learning, the most advanced form of machine learning.
为了应对更棘手的挑战，谷歌正在寻求利用深度学习——机器学习的最高级形式。
What's happened recently is the deep learning approaches have started showing an ability to understand language for many different tasks, he said.
最近出现的情况是，深度学习方法开始在很多不同的任务中表现出了理解语言的能力，他称。
What's more, we'll improve the program through many iterations, gradually incorporating more and more of the core ideas about neural networks and deep learning.
更进一步，我们将通过多次迭代来提升这个程序的效果，逐渐触及越来越多神经网络与深度学习的核心概念。
Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing.
神经网络与深度学习现在为解决许多问题提供了最佳解决方案，例如图像识别、语音识别和自然语言分析。
We use deep learning to try to predict a day in advance when a hard disk is going to fail, and this increases the reliability and reduces the cost of our data centers.
我们使用深度学习来尝试提前预测硬盘会在哪一天出现故障，而这提高了我们的数据中心的可靠性，降低了成本。
With deep learning, researchers can feed huge amounts of data into software systems called neural nets that learn to recognize patterns within the vast information faster than humans.
通过深度学习技术，研究人员能够将海量数据输入“神经元网络”软件系统进行处理，该系统能够以人脑完全无法企及的速度，在海量数据中进行学习和模式分析。
What's the Difference Between Deep Learning Training and Inference?
深度学习训练与推理有何不同？
Deep learning refers to the method of training multi-layer artificial neural networks.
深度学习是指训练多层的人工神经网络的方法。
Deep learning solves this central problem in representation learning by introducing representations that are expressed in terms of other, simpler representations.
通过引入由许多更简单的浅层表达组合得到高层表达，深度学习解决了表达学习这个中心问题。
When driverless cars take the road, the most important road infrastructure will be the low-tech painted-on lane markers that provide critical visual information for the car's deep - learning software.
当无人驾驶汽车上路时，最重要的道路基础设施是车道标志线，这种画在路面上的东西没有什么技术含量，但是这些线将为汽车上的深度学习软件提供关键的图像信息。
When driverless cars take the road, the most important road infrastructure will be the low-tech painted-on lane markers that provide critical visual information for the car's deep - learning software.
当无人驾驶汽车上路时，最重要的道路基础设施是车道标志线，这种画在路面上的东西没有什么技术含量，但是这些线将为汽车上的深度学习软件提供关键的图像信息。
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