conclusions in the futuredeep learning will not only be used by specialists--researchersgraduate studentsand engineers with an academic profile--but will also be tool in the toolbox of every developermuch like web technology today everyone needs to build intelligent appsjust as every business today needs websiteevery product will need to intelligently make sense of user-generated data bringing about this future will require us to build tools that make deep learning radically easy to use and accessible to anyone with basic coding abilities keras is the first major step in that direction the universal machine-learning workflow having access to an extremely powerful tool for creating models that map any input space to any target space is greatbut the difficult part of the machine-learning workflow is often everything that comes before designing and training such models (andfor production modelswhat comes afteras wellunderstanding the problem domain so as to be able to determine what to attempt to predictgiven what dataand how to measure successis prerequisite for any successful application of machine learningand it isn' something that advanced tools like keras and tensorflow can help you with as reminderhere' quick summary of the typical machine-learning workflow as described in define the problemwhat data is availableand what are you trying to predictwill you need to collect more data or hire people to manually label datasetidentify way to reliably measure success on your goal for simple tasksthis may be prediction accuracybut in many cases it will require sophisticated domain-specific metrics prepare the validation process that you'll use to evaluate your models in particularyou should define training seta validation setand test set the validationand test-set labels shouldn' leak into the training datafor instancewith temporal predictionthe validation and test data should be posterior to the training data vectorize the data by turning it into vectors and preprocessing it in way that makes it more easily approachable by neural network (normalizationand so ondevelop first model that beats trivial common-sense baselinethus demonstrating that machine learning can work on your problem this may not always be the casegradually refine your model architecture by tuning hyperparameters and adding regularization make changes based on performance on the validation data onlynot the test data or the training data remember that you should get your model to overfit (thus identifying model capacity level that' greater than you needand only then begin to add regularization or downsize your model licensed to