as result of this wave of investmentthe number of people working on deep learning went in just five years from few hundred to tens of thousandsand research progress has reached frenetic pace there are currently no signs that this trend will slow any time soon the democratization of deep learning one of the key factors driving this inflow of new faces in deep learning has been the democratization of the toolsets used in the field in the early daysdoing deep learning required significant +and cuda expertisewhich few people possessed nowadaysbasic python scripting skills suffice to do advanced deep-learning research this has been driven most notably by the development of theano and then tensorflow--two symbolic tensor-manipulation frameworks for python that support autodifferentiationgreatly simplifying the implementation of new models--and by the rise of user-friendly libraries such as keraswhich makes deep learning as easy as manipulating lego bricks after its release in early keras quickly became the go-to deep-learning solution for large numbers of new startupsgraduate studentsand researchers pivoting into the field will it lastis there anything special about deep neural networks that makes them the "rightapproach for companies to be investing in and for researchers to flock toor is deep learning just fad that may not lastwill we still be using deep neural networks in yearsdeep learning has several properties that justify its status as an ai revolutionand it' here to stay we may not be using neural networks two decades from nowbut whatever we use will directly inherit from modern deep learning and its core concepts these important properties can be broadly sorted into three categoriessimplicity--deep learning removes the need for feature engineeringreplacing complexbrittleengineering-heavy pipelines with simpleend-to-end trainable models that are typically built using only five or six different tensor operations scalability--deep learning is highly amenable to parallelization on gpus or tpusso it can take full advantage of moore' law in additiondeep-learning models are trained by iterating over small batches of dataallowing them to be trained on datasets of arbitrary size (the only bottleneck is the amount of parallel computational power availablewhichthanks to moore' lawis fastmoving barrier versatility and reusability--unlike many prior machine-learning approachesdeep-learning models can be trained on additional data without restarting from scratchmaking them viable for continuous online learning--an important property for very large production models furthermoretrained deep-learning models are repurposable and thus reusablefor instanceit' possible to take deep-learning model trained for image classification and drop it into videoprocessing pipeline this allows us to reinvest previous work into increasingly licensed to