深度神经网络是人工智能的一种流行方式，因为它们能够通过大量数据做出预测。
Deep neural networks are a popular flavor of artificial intelligence, because of their aptitude in being able to make predictions based on large amounts of data.
通俗地讲就是指计算机通过深度神经网络，模拟人脑的机制来进行学习、判断和决策。
Generally speaking, it is the way a computer imitates the human brain to learn, judge and make decisions using deep neural networks.
手臂由两个深度神经网络所控制，它一共有三个关节，并连接着两根用来抓东西的手指。
They have two grasping fingers attached to a triple-joined arm, which are controlled by two deep neural networks.
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.
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.
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.
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.
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.
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.
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.
在此基础上，采用正交设计方法和神经网络进行了岩体地应力场三维变形反演分析，获得了岩体地应力场沿深度的分布规律。
Then a three-dimensional deformation inversion analysis is conducted by orthogonal design method and neural network to acquire the distribution rule of rock body ground stress field at various depths.
本文针对深度域地震资料反演问题提出了神经网络数据驱动岩性参数反演方法。
In view of the issue of seismic data inversion in depth domain, the paper presented the method for inversion of lithologic parameters driven by neural network.
基于遗传神经网络与模态应变能，提出了一种斜裂缝两阶段诊断方法，识别梁体中斜裂缝的位置、角度和深度。
Based on genetic neural network and modal strain energy, a two-stage method for detecting diagonal cracks is proposed to identify the location, Angle and depth of diagonal cracks in beams.
运用LY12CZ的腐蚀实验数据，根据高强铝合金的失效模式（点蚀-晶间腐蚀-剥蚀），建立了对最大腐蚀深度分类的概率神经网络模型，输出结果与实验数据比较吻合。
Make use of available experimental data of LY12CZ aluminum alloy, a probabilistic neural network was developed to classify the maximum corrosion depth ranges based on the material failure mode.
最后通过GA - BP神经网络与拉丁超立方抽样法相结合构建了可控拉深筋主要影响因子h1和H2与极限拉深深度之间的响应面。
Eventually, the response surfaces composed of the CD main influence factor H1, H2 and limit drawing depth are established by the combination of GA-BP neural network and Latin Hypercube.
以神经网络为基本工具，利用其强大的非线性映射能力，并结合有限元法动力分析成果，为预测在复合射孔条件下岩层裂缝扩展深度提供了一条新的途径。
By using ANN and its powerful nonlinear mapping ability and combining production of FEM dynamic analysis, a new way to predicting terrane crack depth of complex fire hole is offered.
在点状激光三维扫描技术中，用线阵CCD采集深度坐标，用前馈型人工神经网络对深度坐标进行非线性修正。
In 3d scanning system based on dot laser, artificial neural networks are used for calibrating the distortion of CCD range sensor.
其次，根据选定的网络结构用MATLAB语言编写了预测混凝土碳化深度的程序，并利用人工神经网络模型分析了混凝土的碳化规律。
Then the paper compiles the program to predict the depth of the concrete carbonization with MATLAB and analyses the rule of the concrete carbonization.
其次，根据选定的网络结构用MATLAB语言编写了预测混凝土碳化深度的程序，并利用人工神经网络模型分析了混凝土的碳化规律。
Then the paper compiles the program to predict the depth of the concrete carbonization with MATLAB and analyses the rule of the concrete carbonization.
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