Beyond Hello World, A Computer Vision Example

超越Hello World，一个计算机视觉的实例

In the previous exercise you saw how to create a neural network that figured out the problem you were trying to solve. This gave an explicit example of learned behavior. Of course, in that instance, it was a bit of overkill because it would have been easier to write the function Y=2x-1 directly, instead of bothering with using Machine Learning to learn the relationship between X and Y for a fixed set of values, and extending that for all values.

But what about a scenario where writing rules like that is much more difficult -- for example a computer vision problem? Let's take a look at a scenario where we can recognize different items of clothing, trained from a dataset containing 10 different types.

在上一个练习中，你看到了如何创建一个神经网络，让它学会图解决某个问题。那是一个计算机习得行为的明确例子。当然，就那个例子而言，运用神经元网络来解决有点大炮轰苍蝇的意思，因为直接写出函数Y=2x-1会更容易，而不是费力地使用机器学习来学习X和Y之间对于一组固定值的关系，并将其扩展到所有值。