The 4 Deep Learning Breakthroughs You Should Know About

Thanks to the strength of the open source community, the second part is getting easier every day. There are many great tutorials on the specifics of how to train and use Deep Learning models using libraries such as TensorFlow -- many of which publications like Towards Data Science publish on a weekly basis. The implication of this is that once you have an idea for how you'd like to use Deep Learning, implementing your idea, while not easy, involves standard "dev" work: following tutorials like the ones linked throughout this article, modifying them for your specific purpose and/or data, troubleshooting via reading posts on StackOverflow, and so on. They don't, for example, require being (or hiring) a unicorn with Ph.D who can code original neural net architectures from scratch and is an experienced software engineer. This series of essays will attempt to fill a gap on the first part: covering, at a high level, what Deep Learning is capable of, while giving resources for those of you who want to learn more and/or dive into the code and tackle the second part.