Скачать с ютуб Use of Transforming Data to High Dimension || Lesson 82 || Machine Learning || Learning Monkey || в хорошем качестве

Use of Transforming Data to High Dimension || Lesson 82 || Machine Learning || Learning Monkey ||

#machinelearning#learningmonkey In this class, we discuss the Use of Transforming Data to High Dimension. Let's take an example and understand the Use of Transforming Data to High Dimension. The example is in one dimension. The data is not linearly separable. Now convert the data to two-dimension by using a function x and square the x. After converting the data is linearly separable. If we apply a support vector machine on this transformed data we get linearly separable data. The same way take an example in two-dimensional data. The data is not linearly separable. Use a function to transform the data into three-dimensional space. Here we are using as x1^2, square root x1x2, and x2^2. After transforming the data is linearly separable. The advantage of changing to a higher dimension is data can be linearly separable. The difficulty here is computationally costly. This computation cost is reduced by using a kernel trick which we discuss in our next class. Link for playlists:    / @learningmonkey   Link for our website: https://learningmonkey.in Follow us on Facebook @   / learningmonkey   Follow us on Instagram @   / learningmonkey1   Follow us on Twitter @   / _learningmonkey   Mail us @ [email protected]