
Chinese: 
大家好，欢迎收看Semicolon，今天我们要讲的激活函数。
根据维基百科，激活函数指的是给定输入数据与输出之间的一种映射手段。
在需要将数据映射为0到1之间的值，或者任何给定数值的时候，激活函数就起到非常重要的作用。
它们能赋予模型非线性，对于结果的影响很大，影响模型的精确性！
激活函数如此重要，我们必须得好好学习。
这里列出来的就是一些非常著名，常用的激活函数：identify，binary step，logistical(或者sigmoid)，TanH，
Arc Tan，ReLU，leaky ReLU，以及SoftMax。
接下来我们看看这些激活函数都干了些什么。

English: 
Hello, everyone welcome to the semicolon. In this tutorial, we're going to learn about activation functions
So according to Wikipedia
activation functions are something which maps a
particular output to a particular set of inputs. So they are used for containing the
output in between zero to one or any given values
They are also used to impart a non-linearity and they are one of the important factors which affect your results and
accuracy of your model, so it is very important that we learn about it. So these are some famous activation functions
Identity activation function, Binary Step activation function
Logistical or sigmoid which we had seen in the last tutorial.
The Tanh activation function, ArcTan ReLU, Leaky ReLU and SoftMax.
So we'll be looking at what each of this does

Chinese: 
Identify激活函数非常简单，你输入X，就给你返回X，
你看，它的图像长这个样子，实在太简单了。
你输入什么数据，就给你返回什么数据。
这是Binaty Step激活函数
如果输入数据大于0，它就返回1
如果输入数据小于0，它就返回0
实质上他就是把所有正数都转换为1，
把所有负数转换为0。
对于Binary Step激活函数，
当你想做一个1-0分类时，它是非常有用的。
非常有用。
这是Logistic 或者叫Sigmoid激活函数，
它的功能就是不管你输入什么样的数据，都给你转换成0-1之间的数据，
就算你输入了一个非常非常大的数字，
它还是给你转换到0-1之间。
在通常的网络结构中是很有用的，

English: 
So identity function is as simple as this, if you have x as your input, it gives you x. There's nothing big in it.
So this is the graph of it and whatever your curve is you will get the exact same curve?
Now binary Step function if
your input is greater than 0 it gives you 1 and if your input is less than 0 it gives you 0. So it takes
all the positive input makes it 1 and all the  negative inputs and make it 0. So
This is the binary step function. It is very useful in classifiers when you want to classify it between 1 and 0,
then this is very useful.
Then we have a logistic or sigmoid and
what this does is whatever your input is it maps it between 0 to 1 so
even if your input is as large as thousands or lakhs or millions
It will map it between 0 to 1
so
that is very useful in neural networks

English: 
because when the input goes out of 0 it may start to
increase exponentially, which may be a problem, so it contains the input between 0 to 1 which is very useful.
Now we have Tanh
activation function which contains the function
from minus 1 to 1 and is similarly
useful to the sigmoid function
you can try out between Tanh and sigmoid and check which accuracy is better and use it accordingly.
So this is the Tanh activation function 2 by 1 plus e power minus 2x minus 1.
And this is the ArchTan function this is just tan inverse of x. So even this contains the number between
somewhere around minus Pi by 2 to plus Pi by 2. So
even this is a kind of replaceable and alternative to sigmoid or Tanh. It does a similar task.

Chinese: 
因为有时如果输入小于0了，
会导致运行过程出现指数级增大的异常，
这可能称为训练过程带来诸多问题。
这个激活能限制数值范围，所以非常有用。
接下来是TanH激活函数。
其能将输入数据映射至-1到1的范围内，
与sigmoid激活函数比较像。
你可以试一试这两个激活函数，
看看哪个激活函数能使得结果更准确，再决定使用哪个。
这是TanH激活函数。
公式就是长的是这个样子。
这是ArcTan激活函数，
其实就是反正切函数。
它能将数据映射到大概-0.5PI到0.5PI之间。
它可以作为Sigmoid激活函数和TanH激活函数的替代或者另外一种选择。
他们的功能看起来都很像。

Chinese: 
接下来我们将一个目前非常流行的激活函数ReLU，
特别是在深度学习领域，
甚至是常规的神经网络中。
它的功能是将输入集中小于0数据全部转换为0，
而大于0的数据，将被保持下来。
实际上，它的功能就是清除负数，将负数清零。
现在我们讲Leaky ReLU，
功能与ReLU非常类似，然而并不是将所有小于0的数据清零，而是将其绝对值降低。
最后我们将Softmax分类器。
这经常是用在基于概率分布的多分类问题中，
当你有4，5个分类输出时，
这个分类器会给你一个概率分布。
你可以根据这个概率分布，找出概率最大的类别，

English: 
Now we have ReLU, which is a very popular one when it comes to deep learning and
even normal Neural Networks.
So what it does is, whenever your function is less than 0 it gives you 0 and
whenever it is greater than 0 it remains as it is.
So what it does is, it removes the negative part of your function and
now we have leaky ReLU, which does a similar job, but
the it doesn't make the negative input 0 just reduces the magnitude of it.
And then, we have our SoftMax
classifier. So this is used to
impart probabilities. When you have 4 or 5
outputs and you pass it through this you get the probability distribution of each
and this is useful for finding out the most probable
Occurrence or the classification

Chinese: 
这表示这个类别是最有可能正确的分类结果。
这就是一些常用的，著名的，非常有用的激活函数。
他们对于你构建的网络模型的精确度有着重要的影响力。
无论何时，当你不知道如何确定哪个激活函数更有效，
那就挨个儿试一试，测一侧它们对结果的影响。
因为你事先永远不知道哪个激活函数最适合你的模型。
这就是此次教程的全部内容。
如果喜欢，请点击订阅！谢谢！

English: 
where the probability of a class is maximum. So these are some famous
activation functions which are very useful and
play an important role in deciding the accuracy of your model.
Whenever you have a doubt that, this activation function might be better
it's always better that you use it and test the accuracy, because you never know which
activation functions will give you the best accuracy.
So that's it for now guys, if this tutorial helped you hit the like button.
Click on the subscribe button if you want to keep watching Thank you.
