DeepMind researchers believe that their work on verification algorithms has the potential to lead to new scalable algorithms for verifying.
Machine learning is mostly about building good function approximations from data. And, when it comes to good approximations, deep learning algorithms have a great following as they are founded on principles of universal approximation. The adoption of these deep neural networks has been high in the past couple of years. But, as these systems scale, new challenges surface. It can be misclassification of an unexamined vulnerability to adversarial attack. There haven’t been many robust verification techniques so far.
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Project walk-through on Convolution neural networks using transfer learning. From 2 years of my master’s degree, I found that the best way to learn concepts is by doing the projects.
Deep Q-Networks have revolutionized the field of Deep Reinforcement Learning, but the technical prerequisites for easy experimentation have barred newcomers until now.
An Unconventional guide to Deep Learning. We reside in a world, where we are constantly surrounded by deep learning algorithms be it for the good or worse cause.
Deep learning on graphs: successes, challenges, and next steps. TL;DR This is the first in a series of posts where I will discuss the evolution and future trends in the field of deep learning on graphs.