深度学习作为人工智能的一个分支，现已被证明是非常有用的。
这就是深度学习，这就是你需要的。
正确使用它的秘诀是：深度学习。
深度学习训练与推理有何不同？
What's the Difference Between Deep Learning Training and Inference?
深度学习是指训练多层的人工神经网络的方法。
Deep learning refers to the method of training multi-layer artificial neural networks.
学习数据正确的特征表达是看待深度学习的一种观点。
The idea of learning the right representation for the data provides one perspective on 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。
This book will teach you many of the core concepts behind neural networks and deep learning.
基于这些原因，我们把这种人工智能的方法叫做深度学习。
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。
IF YOU know what deep learning and functional skills are, then you are already on the way to understanding eduspeak.
当我们说深度学习一样东西时，它意味着你彻底的学会和掌握了它。
When we're talking about learning something deeply, it means you totally learn it and master it.
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。
It seems like every time deep learning is applied to a task, it produces the best-ever results for that task.
总结下来，这本书的主题---深度学习是一种实现人工智能的途径。
To summarize, deep learning, the subject of this book, is an approach to AI.
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。
And you will have a foundation to use neural networks and deep learning to attack problems of your own devising.
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。
Another perspective on deep learning is that it allows the computer to learn a multi-step computer program.
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。
The purpose of this book is to help you master the core concepts of neural networks, including modern techniques for deep learning.
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）。
The quintessential example of a deep learning model is the feedforward deepnetwork or multilayer perceptron (MLP).
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。
In taking on the more intractable challenges, Google is looking to draw on deep learning, the most advanced form of machine learning.
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。
It will use computer vision, sensors and deep learning algorithms to keep track of what customers are picking up off the shelves.
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 big limitation of deep learning is that almost all the value it's creating is in these input-output mappings.
但有了深度学习技术，它可以帮助广告商选择在向用户提供文本的同时，展示一个小图片。
But with our deep learning technology, it can help an advertiser select a small image to show the user along with the text.
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 happened recently is the deep learning approaches have started showing an ability to understand language for many different tasks, he said.
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。
Deep learning solves this central problem in representation learning by introducing representations that are expressed in terms of other, simpler representations.
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。
After working through the book you will have written code that USES neural networks and deep learning to solve complex pattern recognition problems.
要轻松说英语，你必须将每次课重复很多遍，你必须深度地进行英语学习。深度学习，轻松开口。
To speak English easily, you must repeat each lesson many times. You must learn English deeply. Learn deeply, speak easily.
我们通过解决一个具体的问题：交计算机识别手写数字，来学习神经网络与深度学习后面的核心理念。
We'll learn the core principles behind neural networks and deep learning by attacking a concrete problem: the problem of teaching a computer to recognize handwritten digits.
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。
Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing.
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。
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.
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。
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。
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.
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