By PD controller which is used to make system more stable, the system can reach ideal control effects with the feed forward neural learning controller.
PD反馈控制器用于使系统达到稳定，同时和前馈的神经网络学习控制器一起使系统达到理想的控制效果。
And the media or other technologies we use in learning and practicing the craft of reading play an important part in shaping the neural circuits inside our brains.
而用于学习和锻炼阅读技巧的媒体或其他技术，在塑造我们脑中的神经回路这一点上扮演了重要角色。
This sort of learning could take place with neural networks or support vector machines, but another approach is to use decision trees.
这种学习可以使用神经网络或者支持向量机，不过用决策树也可以实现类似的功能。
Learning requires the brain to create new neural networks.
学习需要大脑去创造神经网络。
A neural net that USES this rule is known as a perceptron, and this rule is called the perceptron learning rule.
一个使用这个规则的神经网络称为感知器，并且这个规则被称为感知器学习规则。
Supervised learning is the most common technique for training neural networks and decision trees.
监督学习是训练神经网络和决策树的最常见技术。
This type of learning could probably be carried out with neural networks, though it is hard to imagine that the problem is simple enough for decision trees.
这种类型的学习通常交给神经网络来完成，虽然很难想象，但用决策树来完成这类问题也很简单。
Their learning mechanism is modeled on the brain's adjustments of its neural connections.
它们的学习机制是模仿大脑调节神经连结的原理。
Kandel worked out the neural circuitry that was established during learning and memory, and examined what molecular changes occurred in the cells of that circuit.
坎德尔发现了学习和记忆时建立的神经回路，并研究了回路中细胞的分子变化。
This process - 'neural plasticity' - is essential to learning (our brains change structure when we take on new information).
这一过程——也即神经重塑——是大脑学习的关键。
For instance, a certain kind of basic neural network, the perceptron, is biased to learning only linear functions (functions with inputs that can be separated into classifications by drawing a line).
例如，某些基本的神经网络，它们的感知器只倾向于学习线形函数(通过划一条线可以把函数输入解析到分类系统中)。
It is interesting to note that Neural Networks is an evolution of learning-oriented estimation, in which the method algorithm is trained to behave like a human expert.
有意思的是，神经网络是一种对学习型评估的进化，算法经过训练后其行为就像是一个人类专家。
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。
Learning a new response forms new neural pathways in the brain.
学会一个新的响应在大脑中形成了新的神经路。
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。
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。
The song control system of birds has become an important model for studying neural system related to learning, behavior, and development.
鸣禽的鸣唱控制系统已成为研究神经系统与学习、行为和发育相关的一个重要模型。
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。
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。
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。
As the basic unit of Neural Network, Neural Controllers with different learning rules will result in different control effects for the learning process of synaptic weights.
作为神经网络控制的基本单元，采用不同学习规则的神经元控制器，对神经元的学习过程将产生不同的影响。
It has important theoretical significance and application value how to find an effective learning algorithm of RBF neural networks.
如何找到一种更加行之有效的RBF神经网络学习算法具有重要的理论意义和应用价值。
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.
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。
Josh Kaufman is a noted rapid learning expert and tells us that any practice done within this time frame causes your brain to embed the learning more rapidly into its neural pathways.
乔希·考夫曼是一位著名的快速学习专家，他告诉我们在这个时间段的练习会更快速地嵌入到其神经通路，大脑学起来更快。
This paper applies parallel tangents of nonlinear programming during the weights training of neural network and puts forward a neural network in view of fast learning algorithm.
因此，作者将非线性规划的平行切线算法用于神经网络的权值学习，提出了一种具有快速学习算法的神经网络。
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。
The LEA decision method has been given so that grads methods and Newton methods can be effectively combined in neural networks learning.
在该系统中采用基于LEA判别法的梯度牛顿有效结合神经网络快速学习方法。
This system, possesses the structure of neural net and learning algorithm, addressed as fuzzy neural net FNN.
该系统具有神经网络的结构和学习算法，称模糊神经网络FNN。
The topologic structure and learning algorithm of the rough neural network are given, and the approximation theorem of the rough neural network is presented.
给出了粗糙神经网络的拓朴结构和学习算法以及粗糙神经网络的逼近定理。
The topologic structure and learning algorithm of the rough neural network are given, and the approximation theorem of the rough neural network is presented.
给出了粗糙神经网络的拓朴结构和学习算法以及粗糙神经网络的逼近定理。
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