Welcome to the weekly newsletter of Deep Learning AI and Blockchain Convergence. We hope that this newsletter we appeal to all those interested in Deep Learning developments and its relationship to decentralized consensus architectures.
Two years ago, a Chinese chip-design expert named Micree Zhan was reading China’s seminal science-fiction novel, The Three-Body Problem, by Liu Cixin, while wrestling with how to create a new processor.
In late 2008, economist Robin Hanson and AI theorist Eliezer Yudkowsky conducted an online debate about the future of artificial intelligence, and in particular about whether generally intelligent AIs will be able to improve their own capabilities very quickly (a.k.a. “foom”). James Miller and Carl Shulman also contributed guest posts to the debate.
Our team has been working with Crazyflie platform to demonstrate how deep learning algorithms can be applied to robotics especially a nano aerial vehicles; thanks to Bitcraze development team~ :slight_smile: To veri…
Almost everyone I know says that “backprop is just the chain rule.” Although
that’s basically true, there are some subtle and beautiful things about
automatic differentiation techniques (including backprop) that will not be
appreciated with this dismissive attitude.
This is a guide to the main differences I’ve found between
PyTorch and TensorFlow.
This post is intended to be useful for anyone considering starting a new
project or making the switch from one deep learning framework to another.
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If Big Data provides the pipes then Deep Learning provides the smarts. Deep Learning involves the interplay of Computer Science, Physics, Biology and Psychology. In addition to that, it has the potential to be extremely disruptive.