This series of articles focuses on Deep Learning algorithms, which have been getting a lot of attention in the last few years, as many of its applications take center stage in our day-to-day life. From self-driving cars to voice assistants, face recognition or the ability to transcribe speech into text.
These applications are just the tip of the iceberg. A long path of research and incremental applications has been paved since the early 1940’s. The improvements and widespread applications we’re seeing today are the culmination of the hardware and data availability catching up with computational demands of these complex methods.
In traditional Machine Learning anyone who is building a model either has to be an expert in the problem area they are working on, or team up with one. Without this expert knowledge, designing and engineering features becomes an increasingly difficult challenge. The quality of a Machine Learning model depends on the quality of the dataset, but also on how well features encode the patterns in the data.
Deep Learning algorithms use Artificial Neural Networks as their main structure. What sets them apart from other algorithms is that they don’t require expert input during the feature design and engineering phase. Neural Networks can learn the characteristics of the data.
Deep Learning algorithms take in the dataset and learn its patterns, they learn how to represent the data with features they extract on their own. Then they combine different representations of the dataset, each one identifying a specific pattern or characteristic, into a more abstract, high-level representation of the dataset. This hands-off approach, without much human intervention in feature design and extraction, allows algorithms to adapt much faster to the data at hand.
Neural Networks are inspired by, but not necessarily an exact model of, the structure of the brain. There’s a lot we still don’t know about the brain and how it works, but it has been serving as inspiration in many scientific areas due to its ability to develop intelligence. And although there are neural networks that were created with the sole purpose of understanding how brains work, Deep Learning as we know it today is not intended to replicate how the brain works. Instead, Deep Learning focuses on enabling systems that learn multiple levels of pattern composition.
And, as with any scientific progress, Deep Learning didn’t start off with the complex structures and…