Deep Learning and AI were the buzz words for 2016; by the end of 2017, they have become more frequent and more confusing. So lets try and understand everything one at a time. We will look into the heart of Deep Learning i.e. Most variants of NNets are hard to understand and the underlying architectural components make them all sound (theoretically) and look (graphically) the same. Thanks to Fjodor van Veen from The Asimov Institute, we have a fair representation of the most popular variants of NNet architectures.

It's easy to believe that machine learning is hard. An arcane craft known only to a select few academics. After all, you're teaching machines that work in ones and zeros to reach their own conclusions about the world. You're teaching them how to think! However, it's not nearly as hard as the complex and formula-laden literature would have you believe.

It's easy to believe that machine learning is hard. An arcane craft known only to a select few academics. After all, you're teaching machines that work in ones and zeros to reach their own conclusions about the world. You're teaching them how to think! However, it's not nearly as hard as the complex and formula-laden literature would have you believe. Like all of the best frameworks we have for understanding our world, e.g.