Automated deep learning - finding the right model is half the battle

Deep learning, the branch of AI that uses artificial neural networks to build prediction and pattern matching models from large datasets relevant to a particular application, is having a sizable impact on both consumer and enterprise software. Whether for enabling home appliances to understand and respond to vocal commands or identifying hidden patterns endemic to all malware, deep learning algorithms allow machines to mimic and even improve upon human cognition in ways that are impossible with imperative or declarative programming. Unfortunately, developing deep learning software isn't easy since the models are customized for a particular use. Indeed, developing models is more like making a custom-fitted suit, not off-the-rack clothing in standard sizes. Deep learning encompasses a large category of software, not a general-purpose solution, and describes a broad range of algorithms and network types, each better suited to particular types of problems and data than others.