Artificial neural networks (ANN) in machine learning (artificial intelligence) are complex compounds of algorithms that work in an organized manner to extract labels or results for a given set of data. It is believed that this technology is akin to the biological arrangement of neurons in the human central nervous system (CNS); wherein a set of neurons transfers stimuli to another set which consequently relates them to another set of neurons till the brain gets to interpret the stimuli and instigate a reaction. Each compound of algorithms simulates a human neuron (nerve cell) and it connects with other compounds of brotherly algorithms to make computations and transmit information. Although this comparison [to the brain cells] is informal because we are not quite yet certain how the human central nervous system works its intricacy, thus we cannot simply compare it to the artificial neural network which obviously is incompetent in terms of intelligence, decisiveness, and originality. This is why some folks will tell you computers can never be as smart as humans, or at least cannot have the same type of natural [animate] intelligence, because artificial neural networks which are themselves the modelled brains of these [artificial intelligence] computers don't yet seem to compare to the human brain. However, the artificial intelligence we have today, and the ones we perceive of the future, are very much impressive when it comes to data [or information] processing. This, perhaps, could be the championship of the computer over humans, because today, we can make a 1GB computer process stored data faster than a human student ever could — of course, if students had that superpower as artificial neural networks, they wouldn’t be perplexed remembering things in the exams at all. So, if the artificial neural networks are indeed competent in data processing, an advantage that has yielded a lot of awesomeness in artificial intelligence today, how do they do it?
在机器学习(人工智能) 甲 rtificial神经网络(ANN)是算法络合物，在以有组织的方式来提取标签或工作结果对于给定的数据集。 可以相信，该技术类似于人类中枢神经系统(CNS)中神经元的生物排列。 其中一组神经元将刺激转移到另一组神经，从而将它们与另一组神经元联系起来，直到大脑理解该刺激并引发React。 每种算法的复合物都模拟人的神经元(神经细胞)，并且与兄弟算法的其他复合物连接以进行计算并传输信息。 尽管[与脑细胞]的比较是非正式的，因为我们还不确定人类中枢神经系统如何发挥其复杂性，所以我们不能简单地将其与人工神经网络相比较，后者在智力，果断性，和独创性。 这就是为什么有些人会告诉您计算机永远不会像人类一样聪明，或者至少不能具有相同类型的自然[动画]智能的原因，因为人工神经网络本身就是这些[人工智能]计算机的建模大脑，似乎还不能与人脑相提并论。 但是，当涉及到数据[或信息]处理时，我们今天拥有的人工智能以及我们对未来拥有的人工智能令人印象深刻。 也许这可能是计算机在人类之上的冠军，因为今天，我们可以使1GB的计算机处理数据的速度比人类学生更快—当然，如果学生拥有像人工神经网络那样的超能力，他们将不会完全不要为考试记忆而困惑。 那么，如果人工神经网络确实能够胜任数据处理，而这一优势已经在当今的人工智能领域引起了极大的反响，那么它们将如何做呢？
A typical artificial neural network (the brain of the AI) consists of a few, scores, cents, thousands, or even millions of artificial neurons called units — like the human brain has billions of units of brain cells. The units of the artificial neural network are arranged in a series of layers, each connecting to the layers on either side.
一个典型的人工神经网络(人工智能的大脑)由数个，分数，美分，数千甚至数百万个称为单位的人工神经元组成 ， 就像人类的大脑拥有数十亿个脑细胞单位。 人工神经网络的单元排列成一系列的层，每个层都连接到任一侧的层。
From the image above, we see the arrangement of these layers. The first layer receives data, input, or signals (if a sensor) that the artificial neural network will attempt to recognize and process — thus, it is called the input layer. The next sets of layers just before the last layer are called the hidden layers, consisting of sets of other artificial neurons that take the processed results from the input layer to re-process them. The last layer is the output layer, and it simply outputs the results of the computations from previous layers. Simple artificial neural networks, like a perceptron, consist of one input layer of artificial neurons, one or a few more hidden layers of artificial neurons and an output layer of artificial neurons. Other complex artificial neural networks will consist of tens, hundreds, thousands, and millions of hidden layers so that they get inputs or raw information from one layer (the input layer), process it through a very rich myriad of layers inside, and then give computed results through the last layer (output layer) — these ones are called Deep Neural Networks (DNN).
从上图可以看到这些层的排列。 第一层接收人工神经网络将尝试识别和处理的数据，输入或信号(如果是传感器)，因此被称为输入层 。 紧接在最后一层之前的下一层称为隐藏层 ，由其他人造神经元组成，这些人造神经元从输入层获取经过处理的结果以对其进行重新处理。 最后一层是输出层 ，它仅输出前一层的计算结果。 简单的人工神经网络(如感知器 )由人工神经元的一个输入层，一个或几个隐藏的人工神经元层和一个人工神经元输出层组成。 其他复杂的人工神经网络将由数十，数百，数千和数百万个隐藏层组成，以便它们从一层(输入层)获取输入或原始信息，通过内部非常丰富的层对其进行处理，然后给出经过最后一层(输出层)的计算结果-这些结果称为深度神经网络 (DNN)。
Neural networks, apart from their numerous layers, have more intricate structures and connections between individual artificial neurons of each layer. Each artificial neuron, which means a unit in one layer, could be connected to one or more artificial neurons of the next layer as in the former image. The connections between artificial neurons are represented by individual numbers called weights, which can be either positive or negative, depending on which artificial neuron succeeds the other in the network. The artificial neuron with a larger weight will have a larger influence in the computation over the ones with lesser weights — this happens to be the way brain cells trigger one another across tiny gaps called synapses. The bias is a conventional value attributed to each artificial neuron.
神经网络除了具有无数层外，在每一层的各个人工神经元之间还具有更复杂的结构和连接。 每个人造神经元，即一层中的一个单元，可以连接到下一层的一个或多个人造神经元，如前一个图像所示。 人工神经元之间的联系由称为权重的单个数字表示， 权重可以是正数，也可以是负数，具体取决于网络中哪个人工神经元接替另一个。 权重较大的人工神经元与权重较小的人工神经元相比，对计算的影响更大-这恰好是脑细胞在称为突触的微小间隙中相互触发的方式。 偏差是归属于每个人工神经元的常规值。
Each artificial neuron has its own number associated to it, called activation. This number represents the artificial neuron and is also used in its computation. There is a complex calculation between the activations, weights, and biases of connections to obtain the value (or activation) of a destination artificial neuron in the network. Mathematical functions such as sigmoid function, rectified linear activation unit, ReLU function for short, threshold function, and hyperbolic tangent function are used as the basis of algorithms that work these computations. Each of these functions aims to compute the value of the destination artificial neuron in the network by calculating the sum of the products of connected units with its given bias. The first of the hidden layers obtains values for individual artificial neurons in it and passes these values for the computation of subsequent hidden layers — like a chain reaction in chemistry. This discourse is not intended to dissect the flow of mathematical logic in the computer’s brain as much as it is an overview which allows you to understand how we use machine learning to build the brains of AI— thus, we shall rather briefly discuss the applications of artificial neural networks, something less abstract; I wouldn’t want to bore you with mathematics [in this ‘overview’].
每个人工神经元都有自己的编号，称为激活 。 该数字代表人工神经元，也用于其计算。 在连接的激活，权重和偏差之间要进行复杂的计算，才能获得网络中目标人工神经元的值(或激活)。 使用诸如S形函数 ， 整流线性激活单元，短路的ReLU函数 ， 阈值函数和双曲正切函数等数学函数作为执行这些计算的算法的基础。 这些功能中的每一个旨在通过计算具有给定偏差的连接单元的乘积之和来计算网络中目标人工神经元的值。 隐藏层中的第一层获取其中的各个人工神经元的值，并将这些值传递给后续隐藏层的计算，就像化学中的连锁React一样。 本论述并非旨在剖析计算机大脑中的数学逻辑流，而是作为概述，使您能够了解我们如何使用机器学习来构建AI的大脑，因此，我们将简要讨论以下方面的应用：人工神经网络，不太抽象的东西； 在本“概述”中，我不想对数学感到厌烦。
Artificial Neural Networks (ANN) are used in recognition systems, such as speech recognition, handwriting or text recognition, computer vision, and also natural language processing (which conversational robots use). We cannot list all the applications of neural networks as much as we don’t yet know the limitations of artificial intelligence itself. Artificial neural networks are the brains of artificial intelligence systems, and any computer program professing to apply artificial intelligence but does not have a neural network of algorithms underlying its codes is only like some of the invertebrate animals which live but don’t have brains. Police departments in developed and some developing countries use artificial neural networks in their computer vision systems to identify the registration plate numbers of cars from their traffic cameras. These systems could detect the speed of cars or whether a driver disobeys any of the traffic laws, simply by calculating their velocity mathematically. They capture the plate number of that car which the police will obtain from the system; thus, the law need not be perplexed searching for the criminal or prosecuting the innocent, the AI gets the exact culprit’s plate number with other necessary details.
人工神经网络(ANN)用于识别系统，例如语音识别，手写或文本识别，计算机视觉以及自然语言处理(会话机器人使用)。 我们还不能列出神经网络的所有应用，因为我们还不了解人工智能本身的局限性。 人工神经网络是人工智能系统的大脑，任何自称应用人工智能但没有基于其代码的算法的神经网络的计算机程序，都只像一些无脑但无脑的无脊椎动物。 发达国家和一些发展中国家的警察部门在其计算机视觉系统中使用人工神经网络从交通摄像头中识别汽车的车牌号。 这些系统只需数学计算速度即可检测汽车的速度或驾驶员是否违反任何交通法规。 他们捕获了警察将从系统中获得的那辆车的车牌号； 因此，法律不必为寻找罪犯或起诉无辜者而感到费解，AI会获得肇事者的确切车牌号以及其他必要的细节。
There are several types of artificial neural networks, but the most common ones are Convolutional Neural Network (CNN), Feedforward Neural Network (FNN), Recurrent Neural Network (RNN), Modular Neural Network (MNN), Kohonen Self Organizing Neural Network, Radial basis function Neural Network, etcetera. These networks are inspired by the concept of the biological brain, though we don’t see any significant resemblance. They differ, basically, in their abstract structures, functions, and uses.
人工神经网络有几种类型，但最常见的是卷积神经网络(CNN)，前馈神经网络(FNN)，递归神经网络(RNN)，模块化 神经网络(MNN) ， Kohonen自组织神经网络，径向基函数神经网络等。 这些网络的灵感来自生物大脑的概念，尽管我们没有发现任何明显的相似之处。 基本上，它们的抽象结构，功能和用途不同。
For the most part, a higher knowledge in mathematics is essential for any programmer who desires to engage this field of building brains for AI, but everything seems to have cheaper alternatives. There are python programs that can be written in these neural networks without the programmer having to know all the hows of the mathematical logic and induction that make the algorithms work, therefore, if they so wish, they can learn more about this field. Artificial neural networks, by the way, keeps growing and we see better computations every day because the AI continues to learn. Soon enough, even though AI, in my opinion, will definitely not have the human type of intelligence, there will be computer actions that will be difficult to distinguish from human actions.
在大多数情况下，对于希望参与AI大脑建设这一领域的任何程序员来说，数学知识都是至关重要的，但是一切似乎都有更便宜的选择。 可以在这些神经网络中编写python程序，而程序员不必知道使算法起作用的所有数学逻辑和归纳方法，因此，如果他们愿意的话，他们可以了解有关该领域的更多信息。 顺便说一下，人工神经网络一直在增长，并且由于AI不断学习，我们每天都能看到更好的计算。 很快，我认为即使AI绝对不会具有人类的智能类型，但仍然会有计算机动作难以与人类动作区分开。