Deep Learning: Going Deeper toward Meaningful Patterns in Complex Data

In a previous blog post, we addressed how machine learning is becoming ever more useful in cybersecurity and introduced some basic terms, techniques, and workflows that are essential for those who work in machine learning. Although traditional machine learning methods are already successful for many problems, their success often depends on choosing and extracting the right features from a dataset, which can be hard for complex data. For instance, what kinds of features might be useful, or possible to extract, in all the photographs on Google Images, all the tweets on Twitter, all the sounds of a spoken language, or all the positions in the board game Go? This post introduces deep learning, a popular and quickly-growing subfield of machine learning that has had great success on problems about these datasets, and on many other problems where picking the right features for the job is hard or impossible. A mathematical description of a dataset, with certain properties that can be adjusted to make the description more accurately represent the data, is called a model.