In recent years, there’s been a resurgence in the field of Artificial Intelligence. It’s spread beyond the academic world with major players like Google, Microsoft, and Facebook creating their own research teams and making some impressive acquisitions.
Some this can be attributed to the abundance of raw data generated by social network users, much of which needs to be analyzed, as well as to the cheap computational power available via GPGPUs.
But beyond these phenomena, this resurgence has been powered in no small part by a new trend in AI, specifically in machine learning, known as “Deep Learning”. In this article, I’ll introduce you to the key concepts and algorithms behind Deep Learning, beginning with the simplest unit of composition and building from there.
(For full disclosure: I’m also the author of a Java deep learning library, available here, and the examples in this article are implemented using the above library. If you like it, you can support it by giving it a star on GitHub, for which I would be grateful. Usage instructions are available on the homepage.)