pdfcoffee

Preface
That was only the beginning. Today, DL techniques are successfully applied in
heterogeneous domains including, but not limited to: healthcare, environment,
green energy, computer vision, text analysis, multimedia, finance, retail, gaming,
simulation, industry, robotics, and self-driving cars. In each of these domains, DL
techniques can solve problems with a level of accuracy that was not possible using
previous methods.
It is worth nothing that interest in DL is also increasing. According to the State of
Deep Learning H2 2018 Review [9] "Every 20 minutes, a new ML paper is born.
The growth rate of machine learning papers has been around 3.5% a month [..]
around a 50% growth rate annually." During the past three years, it seems like we
are living during a Cambrian explosion for DL, with the number of articles on our
arXiv growing faster than Moore's Law (see Figure 2). Still, according to the review
this "gives you a sense that people believe that this is where the future value in
computing is going to come from":
Figure 2: ML papers on arXiv appears to be growing faster than Moore's Law (source: https://www.kdnuggets.
com/2018/12/deep-learning-major-advances-review.html)
arXiv is a repository of electronic preprints approved for posting
after moderation, but not full peer review.
[ xiv ]