uses gradient boostinga way to improve any machine-learning model by iteratively training new models that specialize in addressing the weak points of the previous models applied to decision treesthe use of the gradient boosting technique results in models that strictly outperform random forests most of the timewhile having similar properties it may be one of the bestif not the bestalgorithm for dealing with nonperceptual data today alongside deep learningit' one of the most commonly used techniques in kaggle competitions back to neural networks around although neural networks were almost completely shunned by the scientific community at largea number of people still working on neural networks started to make important breakthroughsthe groups of geoffrey hinton at the university of torontoyoshua bengio at the university of montrealyann lecun at new york universityand idsia in switzerland in dan ciresan from idsia began to win academic image-classification competitions with gpu-trained deep neural networks--the first practical success of modern deep learning but the watershed moment came in with the entry of hinton' group in the yearly large-scale image-classification challenge imagenet the imagenet challenge was notoriously difficult at the timeconsisting of classifying highresolution color images into , different categories after training on million images in the top-five accuracy of the winning modelbased on classical approaches to computer visionwas only thenin team led by alex krizhevsky and advised by geoffrey hinton was able to achieve top-five accuracy of %-- significant breakthrough the competition has been dominated by deep convolutional neural networks every year since by the winner reached an accuracy of %and the classification task on imagenet was considered to be completely solved problem since deep convolutional neural networks (convnetshave become the go-to algorithm for all computer vision tasksmore generallythey work on all perceptual tasks at major computer vision conferences in and it was nearly impossible to find presentations that didn' involve convnets in some form at the same timedeep learning has also found applications in many other types of problemssuch as natural-language processing it has completely replaced svms and decision trees in wide range of applications for instancefor several yearsthe european organization for nuclear researchcernused decision tree-based methods for analysis of particle data from the atlas detector at the large hadron collider (lhc)but cern eventually switched to keras-based deep neural networks due to their higher performance and ease of training on large datasets what makes deep learning different the primary reason deep learning took off so quickly is that it offered better performance on many problems but that' not the only reason deep learning also makes licensed to