what is deep learningproblem-solving much easierbecause it completely automates what used to be the most crucial step in machine-learning workflowfeature engineering previous machine-learning techniques--shallow learning--only involved transforming the input data into one or two successive representation spacesusually via simple transformations such as high-dimensional non-linear projections (svmsor decision trees but the refined representations required by complex problems generally can' be attained by such techniques as suchhumans had to go to great lengths to make the initial input data more amenable to processing by these methodsthey had to manually engineer good layers of representations for their data this is called feature engineering deep learningon the other handcompletely automates this stepwith deep learningyou learn all features in one pass rather than having to engineer them yourself this has greatly simplified machine-learning workflowsoften replacing sophisticated multistage pipelines with singlesimpleend-to-end deep-learning model you may askif the crux of the issue is to have multiple successive layers of representationscould shallow methods be applied repeatedly to emulate the effects of deep learningin practicethere are fast-diminishing returns to successive applications of shallow-learning methodsbecause the optimal first representation layer in threelayer model isn' the optimal first layer in one-layer or two-layer model what is transformative about deep learning is that it allows model to learn all layers of representation jointlyat the same timerather than in succession (greedilyas it' calledwith joint feature learningwhenever the model adjusts one of its internal featuresall other features that depend on it automatically adapt to the changewithout requiring human intervention everything is supervised by single feedback signalevery change in the model serves the end goal this is much more powerful than greedily stacking shallow modelsbecause it allows for complexabstract representations to be learned by breaking them down into long series of intermediate spaces (layers)each space is only simple transformation away from the previous one these are the two essential characteristics of how deep learning learns from datathe incrementallayer-by-layer way in which increasingly complex representations are developedand the fact that these intermediate incremental representations are learned jointlyeach layer being updated to follow both the representational needs of the layer above and the needs of the layer below togetherthese two properties have made deep learning vastly more successful than previous approaches to machine learning the modern machine-learning landscape great way to get sense of the current landscape of machine-learning algorithms and tools is to look at machine-learning competitions on kaggle due to its highly competitive environment (some contests have thousands of entrants and milliondollar prizesand to the wide variety of machine-learning problems coveredkaggle offers realistic way to assess what works and what doesn' sowhat kind of algorithm is reliably winning competitionswhat tools do top entrants uselicensed to