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"and dramatically impacted society. usually, when researchers investigate an esoteric and apparently impractical corner of science or engineering, it remains just that — esoteric and impractical. however, this was a notable exception. despite widespread skepticism, the systematic efforts of yoshua bengio, geoffrey hinton, yann lecun, and others eventually paid off. the title of this book is “understanding deep learning” to distinguish it from vol- umes that cover coding and other practical aspects. this text is primarily about the ideas that underlie deep learning. the first part of the book introduces deep learning models and discusses how to train them, measure their performance, and improve this performance. the next part considers architectures that are specialized to images, text, and graph data. these chapters require only introductory linear algebra, calculus, and probability and should be accessible to any second-year undergraduate in a quantitative discipline. subsequent parts of the book tackle generative models and reinforcement learning. these chapters require more knowledge of probability and calculus and target more advanced students. thetitleisalsopartlyajoke—no-onereallyunderstandsdeeplearningatthetimeof writing. modern deep networks learn piecewise linear functions with more regions than thereareatomsintheuniverseandcanbetrainedwithfewerdataexamplesthanmodel parameters. it is neither obvious that we should be able to fit these functions reliably nor that they should generalize well to new data. the penultimate chapter addresses theseandotheraspectsthatarenotyetfullyunderstood. regardless, deeplearningwill changetheworldforbetterorworse. thefinalchapterdiscussesaiethicsandconcludes with an appeal for practitioners to consider the moral implications of their work. your time is precious, and i have striven to curate and present the material so you can understand it as efficiently as possible. the main body of each chapter comprises a succinct description of only the most essential ideas, together with accompanying illustrations. theappendicesreviewallmathematicalprerequisites,andthereshouldbe no need to refer to external material. for readers wishing to delve deeper, each chapter has associated problems, python notebooks, and extensive background notes. writing a book is a lonely, grinding, multiple-year process and is only worthwhile if thevolumeiswidelyadopted. ifyouenjoyreadingthisorhavesuggestionsforimproving it, please contact me via the accompanying website. i would love to hear your thoughts, which will inform and motivate subsequent editions. draft: please send errata to udlbookmail@gmail.com.acknowledgments writingthisbookwouldnothavebeenpossiblewithoutthegeneroushelpandadviceofthesein- dividuals: kathryn hume, kevin murphy, christopher bishop, peng xu, yann dubois, justin domke, chris fletcher, yanshuai cao, wendy tay, corey toler-franklin, dmytro mishkin, guy mccusker, daniel worrall, paul mcilroy, roy amoyal, austin anderson, romero barata de morais, gabriel harrison, peter ball, alf muir, david bryson, vedika parulkar, patryk lietzau, jessica nicholson, alexa huxley, oisin mac aodha, giuseppe castiglione, josh akyl- bekov,alexgougoulaki,joshuaomilabu,alisterguenther,joegoodier,loganwade,joshua guenther, kylan tobin, benedict ellett, jad araj, andrew glennerster, giorgos sfikas, diya vibhakar, sam mansat-bhattacharyya, ben ross, ivor simpson, gaurang aggarwal, shakeel sheikh,jacobhorton,felixrammell,sashaluccioni,akshilpatel,alessandrogentilini,kevin mercier, krzysztoflichocki,chuckkrapf, brianha, chriskang, leonardoviotti, kaili, hi- man abdollahpouri, ari pakman, giuseppe antonio di luna, dan oneată, conrad whiteley, , joseph santarcangelo, brad shook, gabriel brostow, lei he, ali satvaty, romain sabathé, qiang zhou, prasanna vigneswaran, siqi zheng, stephan grein, jonas klesen, giovanni stilo, huang bokai, kevin mcguinness, qiang sun, zakaria lot" |
