what is deep learningalthough machine learning only started to flourish in the sit has quickly become the most popular and most successful subfield of aia trend driven by the availability of faster hardware and larger datasets machine learning is tightly related to mathematical statisticsbut it differs from statistics in several important ways unlike statisticsmachine learning tends to deal with largecomplex datasets (such as dataset of millions of imageseach consisting of tens of thousands of pixelsfor which classical statistical analysis such as bayesian analysis would be impractical as resultmachine learningand especially deep learningexhibits comparatively little mathematical theory--maybe too little--and is engineering oriented it' hands-on discipline in which ideas are proven empirically more often than theoretically learning representations from data to define deep learning and understand the difference between deep learning and other machine-learning approachesfirst we need some idea of what machinelearning algorithms do just stated that machine learning discovers rules to execute data-processing taskgiven examples of what' expected soto do machine learningwe need three thingsinput data points--for instanceif the task is speech recognitionthese data points could be sound files of people speaking if the task is image taggingthey could be pictures examples of the expected output--in speech-recognition taskthese could be human-generated transcripts of sound files in an image taskexpected outputs could be tags such as "dog,"cat,and so on way to measure whether the algorithm is doing good job--this is necessary in order to determine the distance between the algorithm' current output and its expected output the measurement is used as feedback signal to adjust the way the algorithm works this adjustment step is what we call learning machine-learning model transforms its input data into meaningful outputsa process that is "learnedfrom exposure to known examples of inputs and outputs thereforethe central problem in machine learning and deep learning is to meaningfully transform datain other wordsto learn useful representations of the input data at hand--representations that get us closer to the expected output before we go any furtherwhat' representationat its coreit' different way to look at data--to represent or encode data for instancea color image can be encoded in the rgb format (red-green-blueor in the hsv format (hue-saturation-value)these are two different representations of the same data some tasks that may be difficult with one representation can become easy with another for examplethe task "select all red pixels in the imageis simpler in the rg formatwhereas "make the image less saturatedis simpler in the hsv format machine-learning models are all about finding appropriate representations for their input data--transformations of the data that make it more amenable to the task at handsuch as classification task licensed to