what is deep learninghyperplanes in the new representation spaceyou don' have to explicitly compute the coordinates of your points in the new spaceyou just need to compute the distance between pairs of points in that spacewhich can be done efficiently using kernel function kernel function is computationally tractable operation that maps any two points in your initial space to the distance between these points in your target representation spacecompletely bypassing the explicit computation of the new representation kernel functions are typically crafted by hand rather than learned from data--in the case of an svmonly the separation hyperplane is learned at the time they were developedsvms exhibited state-of-the-art performance on simple classification problems and were one of the few machine-learning methods backed by extensive theory and amenable to serious mathematical analysismaking them well understood and easily interpretable because of these useful propertiessvms became extremely popular in the field for long time but svms proved hard to scale to large datasets and didn' provide good results for perceptual problems such as image classification because an svm is shallow methodapplying an svm to perceptual problems requires first extracting useful representations manually ( step called feature engineering)which is difficult and brittle decision treesrandom forestsand gradient boosting machines decision trees are flowchart-like structures that let you classify input data points or predict output values given inputs (see figure they're easy to visualize and interpret decisions trees learned from data began to receive significant research interest in the sand by they were often preferred to kernel methods input data question question category category question category category figure decision reethe parameters hat are learned are the questions about he dat question could befor instanceis coefficient in the dat greater han ?in particularthe random forest algorithm introduced robustpractical take on decision-tree learning that involves building large number of specialized decision trees and then ensembling their outputs random forests are applicable to wide range of problems--you could say that they're almost always the second-best algorithm for any shallow machine-learning task when the popular machine-learning competition website kaggle (became favorite on the platform--until when gradient boosting machines took over gradient boosting machinemuch like random forestis machine-learning technique based on ensembling weak prediction modelsgenerally decision trees it licensed to