Deep Misconceptions About Deep Learning – Towards Data Science

I started this article with the hopes of confronting a few misconceptions about Deep Learning (DL), a field of Machine Learning that is simultaneously labelled a silver bullet and research hype. The truth lies somewhere in the middle, and I hope I can un-muddy the waters -- at least a little bit. Importantly, I hope to clarify some processes to attack DL problems and also discuss why it performs so well in some areas such as Natural Language Processing (NLP), image recognition, and machine-translation while failing at others. Media often portrays Deep Learning as a magical recipe to the end of the world or the solution to all life's problems. In reality, it is anything but. Moreover, while DL has its fair share of strange behaviour and unexplained results, it is ultimately meritocratically driven.