If you are familiar with the terms Artificial Intelligence, Machine Learning , Deep Learning etc , you might know that these so called “Machine Learning Algorithms” have taken the world by storm , infiltrating every possible sector of modern technology and making their implementation rather an obligation than innovation. Such is the power of artificial intelligence. Now some may argue whether the machine learning algorithms we use today are autonomous enough to be called AI , but that is a very broad debate and not the focus of this article.

如果您熟悉“ 人工智能” ，“ 机器学习” ，“ 深度学习”等术语，您可能会知道这些所谓的“机器学习算法”席卷了整个世界，渗入了现代技术的各个领域，并使它们的实施成为一项义务。比创新。 这就是人工智能的力量。 现在有些人可能会争论我们今天使用的机器学习算法是否足够自主，可以称为AI，但这是一个非常广泛的争论，而不是本文的重点。

In this article, I have tried to explore the deep intricacies and relationship between neural networks and the human mind.

在本文中，我试图探索神经网络与人类思维之间的深层错综复杂关系。

How is it that when you start typing something on Google , the browser already guesses what you’re looking for? How does your mobile camera recognize a face in the vast ocean of minuscule pixels that we call a picture?How does a virtual assistant like Alexa, Siri or Google Home achieve complex tasks like scheduling meetings, fetching weather information, reading bedtime stories, etc ? The answer to all of this, as you may have guessed - is Machine Learning. More specifically, Neural Networks.

当您开始在Google上输入内容时，浏览器已经猜到您要查找的内容了吗？ 您的移动摄像头如何识别我们称之为照片的微小像素海洋中的面Kong？Alexa，Siri或Google Home之类的虚拟助手如何完成诸如安排会议，获取天气信息，阅读就寝故事等复杂任务？ 您可能已经猜到了，所有这些的答案是机器学习。 更具体地， 神经网络 。

什么是神经网络？ (What is a Neural Net?)

The purpose of this segment is to teach someone completely new to Deep Learning what a basic Neural Network is. Although the actual math involved is way more complex, the highlight here is not how they work , but to try to understand their connection to the human mind.

本部分的目的是教给完全不接触深度学习的人什么是基本的神经网络。 尽管实际涉及的数学要复杂得多，但这里的重点不是它们的工作原理， 而是试图了解它们与人的思想的联系。

The above is the representation of a neuron- the simplest unit in a neural network. What a neuron does is, it takes in some input values from all the neurons in the previous layer (in this case the previous layer to our neuron(y) is the blue one i.e. x1, x2 , x3..) and multiplies each input with the weight associated with the neuron the input is coming from. So here, x1 is multiplied with w1 , x2 is multiplied with w2 and so on. All these multiplications are added inside the neuron y.

上面是神经元的表示-神经网络中最简单的单位。 神经元所做的是，它从前一层的所有神经元中获取一些输入值(在这种情况下，我们神经元(y)的前一层是蓝色的，即x1，x2，x3 ..)，并将每个输入相乘与来自输入的神经元相关的权重。 所以在这里，x1乘以w1，x2乘以w2，依此类推。 所有这些乘法都加到神经元y内。

y=x1.w1 + x2*w2 +x3*w3……xm.wm

y = x1.w1 + x2 * w2 + x3 * w3……xm.wm

This summation is then converted to an output by using activation functions such as sigmoid, tanH , reLU etc. What are these activation functions? Let me explain by giving you an example of the sigmoid activation function.

然后，通过使用诸如Sigmoid，tanH，reLU等激活函数 ，将该求和结果转换为输出。 这些激活函数是什么？ 让我通过给您一个S型激活函数的例子进行解释。

The sigmoid function converts any input (in our case , the summation-y is the input to the sigmoid function) to a value in the range 0 to 1. As simple as that. You add the products of the inputs and the respective weights , pass them through the activation function , and voila! your neuron has given you an output. Now that we know about neurons , let’s see how networks use them.

sigmoid函数将任何输入(在本例中为sigmoid函数的输入y)转换为0到1范围内的值。 您将输入乘积和各个权重的乘积相加，将它们通过激活函数传递，瞧！ 你的神经元给了你输出。 现在我们了解了神经元，让我们看看网络如何使用它们。

This is as simple as neural networks get. Each grey circle here is a neuron and as you can see, there are three layers in this network -the input layer, the hidden layer and the output layer.

这就像神经网络一样简单。 这里的每个灰色圆圈都是一个神经元，正如您所看到的，此网络中包含三层-输入层，隐藏层和输出层。

Now neural networks learn to do tasks by themselves, but how do they learn? through data of course. We will discuss more about the learning from data in the next segment but know for now , know that data is fed to the neural network through the input layer , processed in the middle layers and then outputted by the output layer.

现在，神经网络学会了自己完成任务，但是他们如何学习呢？ 通过数据当然。 我们将在下一部分中讨论更多有关从数据中学习的知识，但到目前为止，我们知道数据已经通过输入层馈送到神经网络，在中间层进行处理，然后由输出层输出。

For example if you want a network to recognize cats and dogs from images, the labelled images(labelling images means that we give the neural network the correct answer) of the cats and dogs are fed to the neural net, processed , and the layer makes a guess as to whether it is a cat or a dog. Now since it has the answer to the image , it adjusts the weights between the neurons in such a way, that its accuracy increases.

例如，如果您希望网络从图像中识别猫和狗，则将猫和狗的带标签图像(带标签的图像表示我们给神经网络提供了正确的答案)，然后将其喂入神经网络，进行处理，然后该图层将对它是猫还是狗的猜测。 现在，由于它具有图像的答案，因此可以通过这种方式调整神经元之间的权重，从而提高准确性。

人的心灵-巨大的神经网络 (The Human Mind- A giant neural network)

Have you ever observed a toddler playing? How they are always so curious and so hungry for interaction with the world around them. The brain of a toddler is like a simple and untrained neural network which starts learning as soon as they are born. Since the network is very simple in early stages , the first things that the toddler learns are accomplishments of very basic tasks such as moving, eating, crawling etc. For example-

您有没有观察到小孩在玩游戏？ 他们如何总是如此好奇和渴望与周围的世界互动。 幼儿的大脑就像一个简单且未经训练的神经网络 ，一旦出生就开始学习。 由于网络在早期非常简单，因此幼儿学到的第一件事就是完成非常基本的任务，例如移动，进食，爬行等。例如-

When a toddler holds a ball, he observes it. Its weight, its shape, how he feels holding it , the curvature around his skin etc. This observation is part of his initial learning process . This observation is in fact ,what helps him learn. The experience of the toddler with the ball trains his brain for the next time he encounters one. Now when he sees a ball, he remembers what it is and how it will feel to hold and throw it. As the child plays more and more with the ball, his training builds . A teenager is almost an expert at handling and throwing balls and although one may think of throwing balls as a very simple task, the learning that you had to go through for it, was anything but simple.

当幼儿握着球时，他会观察到它。 它的重量，形状，握感，皮肤的弯曲度等。这种观察是他最初学习过程的一部分。 这种观察实际上是什么，可以帮助他学习。 蹒跚学步的球经验会在他下次遇到球时训练他的大脑。 现在，当他看到一个球时，他会记住它是什么以及握住和扔出球的感觉。 随着孩子越来越多地玩球，他的训练就越来越多了。 十几岁的孩子几乎是处理和扔球的专家，尽管人们可能会认为扔球是一项非常简单的任务，但是您必须经过的学习却并不简单。

As i said earlier, the brain of a child is like a simple neural network achieving basic tasks like walking , eating , speaking etc. As the child grows, he/she learns more complex processes such as hand eye co-ordination , speaking fluently, eating without spilling etc. People with experience in training neural networks will agree that it is relatively easy to train a simple network , and as you add more layers and make the data and the end output more complex, the learning rate slows down. Childhood is the best time to learn a new skill because the simple neural net which is the brain is relatively untrained as compared to an adult and thus it becomes easier to bend the neurons to your will. When the same child grows up, the learning becomes difficult with the complexity of the task. Complex problem solving, decision making and judgement making are few examples of complex tasks that even we as adults do not stop learning and building upon.

正如我之前所说，孩子的大脑就像一个简单的神经网络，可以完成诸如步行，进食，说话等基本任务。随着孩子的成长，他/她学会了更复杂的过程，例如手眼协调，流利的口语，有训练神经网络经验的人们会同意，训练一个简单的网络相对容易，并且随着您添加更多的图层并使数据和最终输出变得更加复杂，学习速度会降低。 童年是学习新技能的最佳时机，因为与成年人相比，作为大脑的简单神经网络相对不受训练，因此更容易使神经元屈服于您的意愿。 当同一个孩子长大时，学习变得很困难，因为任务很复杂。 复杂的问题解决，决策和判断力是复杂任务的极少数例子，即使我们作为成年人也不会停止学习和建立。

Okay so now we know that the brain learns all the time. But how exactly does this learning happen?

好吧，现在我们知道大脑一直在学习。 但是这种学习到底是如何发生的呢？

The answer is- the brain rewires itself. Just as the weights and connection between neurons change in a neural network, the brain also modifies the connections between its billions and billions of neurons to give rise to complex networks that have the power to not only retain the training they have undergone earlier, but to also build on that training , and to increase the complexity of the end process.

答案是- 大脑重新连接自己 。 就像神经网络中神经元之间的权重和连接发生变化一样，大脑也会修改其数十亿和数十亿个神经元之间的连接，从而产生复杂的网络，这些网络不仅能够保留他们之前接受的训练，而且能够还要以该培训为基础，并增加最终过程的复杂性。

Even as you are reading this article, every neuron in your brain is changing itself, modifying it’s connections with the billions of other neurons , building on the experience you have gained since you were born. This is how learning happens. Every time you learn, your brain rewires itself! This process is just like the training of a neural network.

即使您在阅读本文时，大脑中的每个神经元都在改变自己，并根据自出生以来获得的经验来改变与数十亿其他神经元的联系。 这就是学习的过程。 每次学习时，您的大脑都会自动重排！ 这个过程就像训练神经网络一样。

数据就是一切 (Data is everything)

You must have heard the term ‘big data’ and you might know that today, nothing is more important to tech giants than data of the consumer base. Why is this so ? Because data is what drives the ‘machine learning algorithms’ . Because data is what trains the neural networks, because without data , learning is impossible.

您一定已经听说过“大数据”一词，并且您可能知道，今天，对于科技巨头而言，没有什么比消费者群的数据更重要了。 为什么会这样呢？ 因为数据驱动着“机器学习算法”。 因为数据是训练神经网络的东西，所以如果没有数据，学习是不可能的。

You must have come across verification captchas on the internet like the one below.

您必须像以下一样在互联网上遇到验证验证码。

Answering the above captcha might not seem like a big deal to you but ask a child to do it and although they may get it right, the answer will come slow.

回答上述验证码对您来说似乎并不重要，但请孩子做，尽管他们可能做对了，但答案会很慢。

Fun fact: did you know that every time you answer a captcha like this, you are helping to train the bots or the neural networks in place, all around the internet! You are in fact helping prepare data to train the models that actually serve you! Next time your ML enthusiast friend starts bragging about the models he has made, tell him that you have trained big and important models yourself.

有趣的事实：您知道吗，每次您回答这样的验证码时，您都将在整个互联网上训练机器人或神经网络！ 实际上，您正在帮助准备数据以训练实际为您服务的模型！ 下次您的ML爱好者朋友开始吹嘘自己制作的模型时，请告诉他您自己训练了重要的重要模型。

Just like the data we prepare for our artificially made models is of utmost importance , the human mind is also a greedy network wanting to be fed data, in huge amounts, every single moment of your life. The data is building you, helping you learn , turning you into the intelligent machine you are. And this brings us to the final aspect of a human mind learning from data around it, the input layer or the five senses.

就像我们为人工模型准备的数据至关重要一样，人的心智也是一个贪婪的网络，希望在生命的每一刻都获得大量数据。 数据正在建立您，帮助您学习，将您变成自己的智能机器。 这使我们进入了人类思维的最后一个方面，即从周围的数据， 输入层或五种感官中学习 。

五种感官 (The five senses)

We know that a neural network has a input layer , through which the data enters the network in a form that the model can understand and process such as- tensors . Data then passes to the hidden layers where the training happens , weights change and finally the output layer gives out a prediction whether it be a cat/dog classification , or completing a sentence based on previous words.

我们知道神经网络有一个输入层，数据通过输入层以模型可以理解和处理张量的形式进入网络。 然后，数据传递到进行训练，权重发生变化的隐藏层，最后输出层给出预测是猫还是狗分类，或者根据先前的单词完成句子。

Since we have compared the human mind to a giant neural network, it is only fair if this network has a input layer as well. The five senses of sight, sound, touch , taste and smell make up the input layer to our mind.The sense of sight is the most important of all the five senses, because the maximum variation in data comes through this sense. A good dataset to train your neural network on is one which contains different types and examples of data rather than monotonous data. The latter type can cause overfitting to the model which means that the model may give correct results when the data is of the same type as the one it was trained on, but when data deviates from the training data norms, the accuracy takes a severe hit. Our eyes are the most active when we are learning most of the important processes in our life. From walking to eating, from learning to ride a bicycle to playing a piano, from interacting with other fellow humans to typing on a computer , almost every task requires our eyes. The sense of sound is also pretty important but it was more so back in the days when humans had to hunt for food in the wilderness focusing their ears for sounds of prey because their survival depended on this sense. Touch is also among the most valuable input senses that humans possess. It keeps our hands away from hot stoves, prevents us from feeding boiling milk to our babies and helps interact with every object we come into contact with. Although the sense of smell and taste do not contribute as much to the major learning of a human neural net, they have their own value. These senses contribute more to the finer pleasures of life than the growing and learning process. The smell of fresh flowers , the taste of good wine and such finer experiences of life come through these senses of smell and taste.

由于我们已将人的思维与巨大的神经网络进行了比较，因此只有该网络也具有输入层时才是公平的。 视觉，声音，触觉，味觉和嗅觉这五种感觉构成了我们大脑的输入层。视觉是这五种感觉中最重要的，因为数据的最大变化是通过这种感觉来实现的。 一个好的神经网络训练数据集是一个包含不同类型和示例的数据，而不是单调数据的数据集。 后一种类型可能导致模型过度拟合，这意味着当数据与被训练的数据类型相同时，该模型可能会给出正确的结果，但是当数据偏离训练数据规范时，准确性将受到严重打击。 当我们学习生活中的大多数重要过程时，我们的眼睛最活跃。 从走路到吃饭，从学习骑自行车到弹钢琴，从与其他同伴互动到在计算机上打字，几乎每个任务都需要我们的眼睛。 声音的感觉也很重要，但在人类不得不在野外觅食的日子里，尤其是因为人们的生存依赖于这种感觉，这种感觉就更加重要了。 触摸也是人类拥有的最有价值的输入感觉之一。 它使我们的手远离火炉，防止我们将沸腾的牛奶喂给婴儿，并有助于与我们接触的每个物体进行交互。 尽管嗅觉和味觉对人类神经网络的主要学习贡献不大，但它们具有自己的价值。 这些感官比成长和学习的过程更能带来美好的生活乐趣。 鲜花的气味，美酒的味道以及美好的生活经历都来自于这些嗅觉和味道。

Now that we know about the input layer , the data , and the neural network where all the processing takes place, let us take an example putting all the pieces of the puzzle together to see the bigger picture i.e. pattern recognition.

现在我们知道了进行所有处理的输入层，数据和神经网络，下面让我们以一个示例为例，将所有难题组合在一起，以查看更大的图片，即模式识别 。

模式识别 (Pattern Recognition)

Let us take the example of learning to read. The eyes are the most important sense here. A child learns to read by learning the alphabets first. He does so by repeatedly looking at the letters,trying to pronounce them and writing them. Each epoch of this reading, speaking and writing trains his mind, improving upon his accuracy until he perfects the art of writing the alphabets. After his network has undergone this relatively simple training, he now learns to string these words together , and here , the sense of sound takes a major role. When he speaks these works , he starts recognizing patterns in these words, how different combinations of the same set of 26 letters give rise to the multitude of words making up the English language. This pattern recognition is very important because this is what helps a child recognize a human face or an animal or a toy. In adults , these pattern recognition becomes very very powerful. The vast amount of training they have undergone and the vast amounts of data they have processed helps them see patterns all around them. Predicting whether it is going to rain based on past experiences with rain is pattern recognition. Connecting memories and experiences to predict the outcome to an event is pattern recognition. Sherlock Holmes finding out that the man in the caravan is Professor Moriarty just by looking at the dust of chalk on his sleeve is pattern recognition!

让我们以学习阅读为例。 眼睛是这里最重要的感觉。 一个孩子通过首先学习字母来学习阅读。 他这样做是通过反复查看字母，然后尝试发音并编写字母。 阅读，口语和写作的每个时代都会训练他的思维，提高他的准确性，直到他完善了写字母的艺术。 在对网络进行了相对简单的培训之后，他现在学习将这些单词组合在一起，在这里，声音感起着重要的作用。 当他讲这些作品时，他开始识别这些单词中的模式，即同一组26个字母的不同组合如何产生构成英语的众多单词。 模式识别非常重要，因为这有助于儿童识别人脸，动物或玩具。 在成年人中，这些模式识别变得非常强大。 他们接受了大量的培训，处理过的大量数据帮助他们查看了周围的模式。 根据过去的降雨经验来预测是否要下雨是模式识别。 模式识别是将记忆和经验联系起来以预测事件的结果。 夏洛克·福尔摩斯(Sherlock Holmes)仅看袖子上的粉笔尘就发现大篷车上的那个人是莫里亚蒂(Moriarty)教授，这是模式识别！

结论 (Conclusion)

I would like to say that though i have first explained how neural networks work and then compared the human mind to them , it is actually the other way around. The human mind inspired the neural networks. Why do you think those tiny functions are called neurons anyway. The human mind is an incredibly complex structure that produces every thought, action, memory, feeling and experience in this world. It is nothing short of miraculous that evolution gave us humans such a powerful tool which we today try to replicate into artificial minds ,which we otherwise call- neural networks.

我想说的是，尽管我首先解释了神经网络是如何工作的，然后将人类的思维与它们进行了比较，但实际上是相反的。 人类的思想激发了神经网络。 为什么您认为这些微小的功能仍然被称为神经元。 人的思维是一个非常复杂的结构，它产生了这个世界上的所有思想，动作，记忆，感觉和经验。 进化为人类提供了如此强大的工具，这真是奇迹般的，我们今天试图将其复制到人工的思想中，否则我们将其称为神经网络。