Introduction to Deep Learning

Abstract

With advances in hardware and the emergence of big data, more advanced computing methods have become increasingly popular. Increasing consumer demand for better products and companies seeking to leverage their resources more efficiently have also been leading this push. In response to these market forces, we have recently seen a renewed and widely spoken about interest in the field of machine learning. At the cross-section of statistics, mathematics, and computer science, machine learning refers to the science of creating and studying algorithms that improve their own behavior in an iterative manner by design. Originally, the field was devoted to developing artificial intelligence, but due to the limitations of the theory and technology that were present at the time, it became more logical to focus these algorithms on specific tasks. Most machine learning algorithms as they exist now focus on function optimization, and the solutions yielded don’t always explain the underlying trends within the data nor give the inferential power that artificial intelligence was trying to get close to. As such, using machine learning algorithms often becomes a repetitive trial and error process, in which the choice of algorithm across problems yields different performance results. This is fine in some contexts, but in the case of language modeling and computer vision, it becomes problematic.