staying up to date in fast-moving field as final parting wordsi want to give you some pointers about how to keep learning and updating your knowledge and skills after you've turned the last page of this book the field of modern deep learningas we know it todayis only few years olddespite longslow prehistory stretching back decades with an exponential increase in financial resources and research headcount since the field as whole is now moving at frenetic pace what you've learned in this book won' stay relevant foreverand it isn' all you'll need for the rest of your career fortunatelythere are plenty of free online resources that you can use to stay up to date and expand your horizons here are few practice on real-world problems using kaggle one effective way to acquire real-world experience is to try your hand at machinelearning competitions on kaggle (through practice and actual coding--that' the philosophy of this bookand kaggle competitions are the natural continuation of this on kaggleyou'll find an array of constantly renewed data-science competitionsmany of which involve deep learningprepared by companies interested in obtaining novel solutions to some of their most challenging machine-learning problems fairly large monetary prizes are offered to top entrants most competitions are won using either the xgboost library (for shallow machine learningor keras (for deep learningso you'll fit right inby participating in few competitionsmaybe as part of teamyou'll become more familiar with the practical side of some of the advanced best practices described in this bookespecially hyperparameter tuningavoiding validation-set overfittingand model ensembling read about the latest developments on arxiv deep-learning researchin contrast with some other scientific fieldstakes places completely in the open papers are made publicly and freely accessible as soon as they're finalizedand lot of related software is open source arxiv (for physicsmathematicsand computer science research papers it has become the de facto way to stay up to date on the bleeding edge of machine learning and deep learning the large majority of deep-learning researchers upload any paper they write to arxiv shortly after completion this allows them to plant flag and claim specific finding without waiting for conference acceptance (which takes months)which is necessary given the fast pace of research and the intense competition in the field it also allows the field to move extremely fastall new findings are immediately available for all to see and to build on an important downside is that the sheer quantity of new papers posted every day on arxiv makes it impossible to even skim them alland the fact that they aren' peer reviewed makes it difficult to identify those that are both important and high quality licensed to