An Introduction to Deep Learning and Neural Networks - insideBIGDATA

It seems as if not a week goes by in which the artificial intelligence concepts of deep learning and neural networks make it into media headlines, either due to an exciting new use case or in an opinion piece speculating whether such rapid advances in AI will eventually replace the majority of human labor. Deep learning has improved speech recognition, genomic sequencing, and visual objection recognition, among many other areas. The availability of exceptionally powerful computer systems at a reasonable cost, combined with the influx of large swathes of data that define the so-called Age of Big Data and the talents of data scientists, have together provided the foundation for the accelerated growth and use of deep learning and neural networks. Companies are now beginning to adopt AI frameworks and libraries, such as MxNet, which is a deep learning framework that gives users the ability to train deep learning models using a variety of languages. There are also dedicated AI platforms aimed at supporting data scientists in deep learning modeling and training which professionals can integrate into their workflows.