the fundamental trick in deep learning is to use this score as feedback signal to adjust the value of the weights littlein direction that will lower the loss score for the current example (see figure this adjustment is the job of the optimizerwhich implements what' called the backpropagation algorithmthe central algorithm in deep learning the next explains in more detail how backpropagation works input weights layer (data transformationweights layer (data transformationweight update predictions yoptimizer true targets loss function loss score figure the loss score is used as feedback signal to adjust he weight initiallythe weights of the network are assigned random valuesso the network merely implements series of random transformations naturallyits output is far from what it should ideally beand the loss score is accordingly very high but with every example the network processesthe weights are adjusted little in the correct directionand the loss score decreases this is the training loopwhichrepeated sufficient number of times (typically tens of iterations over thousands of examples)yields weight values that minimize the loss function network with minimal loss is one for which the outputs are as close as they can be to the targetsa trained network once againit' simple mechanism thatonce scaledends up looking like magic what deep learning has achieved so far although deep learning is fairly old subfield of machine learningit only rose to prominence in the early in the few years sinceit has achieved nothing short of revolution in the fieldwith remarkable results on perceptual problems such as seeing and hearing--problems involving skills that seem natural and intuitive to humans but have long been elusive for machines in particulardeep learning has achieved the following breakthroughsall in historically difficult areas of machine learningnear-human-level image classification near-human-level speech recognition near-human-level handwriting transcription improved machine translation licensed to