conclusions image classificationvastly improved machine translationand more the hype may (and likely willrecedebut the sustained economic and technological impact of deep learning will remain in that sensedeep learning could be analogous to the internetit may be overly hyped up for few yearsbut in the longer term it will still be major revolution that will transform our economy and our lives ' particularly optimistic about deep learning because even if we were to make no further technological progress in the next decadedeploying existing algorithms to every applicable problem would be game changer for most industries deep learning is nothing short of revolutionand progress is currently happening at an incredibly fast ratedue to an exponential investment in resources and headcount from where standthe future looks brightalthough short-term expectations are somewhat overoptimisticdeploying deep learning to the full extent of its potential will take well over decade how to think about deep learning the most surprising thing about deep learning is how simple it is ten years agono one expected that we would achieve such amazing results on machine-perception problems by using simple parametric models trained with gradient descent nowit turns out that all you need is sufficiently large parametric models trained with gradient descent on sufficiently many examples as feynman once said about the universe"it' not complicatedit' just lot of it " in deep learningeverything is vectoreverything is point in geometric space model inputs (textimagesand so onand targets are first vectorizedturned into an initial input vector space and target vector space each layer in deep-learning model operates one simple geometric transformation on the data that goes through it togetherthe chain of layers in the model forms one complex geometric transformationbroken down into series of simple ones this complex transformation attempts to map the input space to the target spaceone point at time this transformation is parameterized by the weights of the layerswhich are iteratively updated based on how well the model is currently performing key characteristic of this geometric transformation is that it must be differentiablewhich is required in order for us to be able to learn its parameters via gradient descent intuitivelythis means the geometric morphing from inputs to outputs must be smooth and continuous-- significant constraint the entire process of applying this complex geometric transformation to the input data can be visualized in by imagining person trying to uncrumple paper ballthe crumpled paper ball is the manifold of the input data that the model starts with each movement operated by the person on the paper ball is similar to simple geometric transformation operated by one layer the full uncrumpling gesture sequence is the complex transformation of the entire model deep-learning models are mathematical machines for uncrumpling complicated manifolds of high-dimensional data richard feynmaninterviewthe world from another point of viewyorkshire television licensed to