2018 IS SET to be an exciting year for businesses seeking to harness the power of deep learning on their journey towards intelligent enterprise . We have taken a look at some of the challenges to overcome and predictions for its implementation from experts in the field who envision it becoming more practical and useful , automating some jobs and augmenting many others , combining machine learning and big data for fresh actionable insights .
A deep learning system is , in short , a multi-layered neural network that learns representations of the world and stores them as a nested hierarchy of concepts many layers deep . For example , when processing thousands of images of human faces , it recognises objects based on a hierarchy of simpler building blocks : straight lines and curved lines at the basic level ; then eyes , mouths , and noses ; entire faces ; and finally , specific facial features .
Besides image recognition , deep learning offers the potential to approach complex challenges such as speech comprehension , human-machine conversation , language translation , and vehicle navigation , amongst others . How can we expect this technology to be implemented in the coming year ?
Demystifying Neural Nets “ Deep neural networks , which mimic the human brain , have demonstrated their ability to ‘ learn ’ from image , audio , and text data ,” says Anand Rao , Innovation Lead in PwC ’ s Analytics Group . However , he says that even after deep neural networks have been in use for over a decade , there is still a lot to learn such as how these networks learn and why they perform so well .
“ That may be changing , thanks to a new theory that applies the principle of an information bottleneck to deep learning ,” he says . “ In essence , it suggests that after an initial fitting phase , a deep neural network will ‘ forget ’ and compress noisy data – that is , data sets containing a lot of additional meaningless information – while still preserving information about what the data represents . Understanding precisely how deep learning works enables its greater development and use . For example , it can yield insights into optimal network design and architecture choices , while providing increased
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