大家除了 AI 之外,听的最多的应该就是机器学习和深度学习了。
今天我们用很容易理解的方式介绍机器学习是什么。
易理解版本
首先,机器学习不是某种具体的算法,而是很多算法的统称。
就好像有人说“我喜欢吃蔬菜”,但是你还是不知道他具体喜欢吃什么,因为蔬菜包含了很多东西,机器学习也是如此。
机器学习怎么理解呢?
假如我们正在教小朋友识字(一、二、三)。我们首先会拿出3张卡片,然后便让小朋友看卡片,一边说“一条横线的是一、两条横线的是二、三条横线的是三”。
不断重复上面的过程,小朋友的大脑就在不停的学习。
当重复的次数足够多时,小朋友就学会了一个新技能——认识汉字:一、二、三。
我们用上面人类的学习过程来类比机器学习。机器学习跟上面提到的人类学习过程很相似。
上面提到的认字的卡片在机器学习中叫——训练集
上面提到的“一条横线,两条横线”这种区分不同汉字的属性叫——特征
下朋友不断学习的过程叫——建模
学会了识字后总结出来的规律叫——模型
通过训练集,不断识别特征,不断建模,最后形成有效的模型,这个过程就叫“机器学习”!
准确版本
Machine learning—— Wikipedia
Machine learning (ML) is the study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.[1][2]:2 Machine learning algorithms are used in the applications of email filtering, detection of network intruders, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning, and focuses on exploratory data analysis through unsupervised learning.[3][4] In its application across business problems, machine learning is also referred to as predictive analytics.
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