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Advice for Short-term Machine Learning Research Project

rockt.github.io · 1,720 words · saved by 1 readers

Every year we get contacted by students who wish to work on short-term machine learning research projects with us. By now, we have supervised a good number of them and we noticed that some of the advice that we gave followed a few recurring principles. In this post, we share what we believe is good advice for a master’s thesis project or a summer research internship in machine learning. This post is by no means comprehensive but instead emphasizes those pitfalls that we saw over and over again. For instance, we will not talk about how to pick a good project or how to generally approach a machine learning research project. Some of our advice is generally applicable for working on machine learning and specifically deep and/or reinforcement research projects. However, some of it is only important when faced with the time constraints of a three-month project and are considerably less important when you just started the journey of a three to five year Ph.D. degree. Machine learning and spec

Tim Rocktäschel Tim Rocktäschel Artificial General Intelligence, Open-endedness, Self-Improvement, Reinforcement Learning Spatial Lorem ipsum dolor sit amet nullam consequat interdum vivamus donce sed libero. Get Started --> – Tim Rocktäschel, Jakob Foerster and Greg Farquhar , 29/08/2018 Every year we get contacted by students who wish to work on short-term machine learning research projects with us. By now, we have supervised a good number of them and we noticed that some of the advice that we gave followed a few recurring principles. In this post, we share what we believe is good adv

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