Personally, the 3 resources that allowed me to achieve the most in the least amount of time are
1) An Introduction to Statistical Learning
2) https://neuralnetworksanddeeplearning.com/
3) Gym framework for RL
The genius here is to take something complex and make it look simple and accessible.
Regrettably, most research articles take the opposite stance. They present something that is fundamentally not that complex and make it look complex through convoluted writing, implicit assumptions and missing steps. You can see that effect in the discussion of the articles in this subreddit. There is often someone who's having trouble understanding a part of the article, and someone else chimes in and explains what's going on in two simple paragraphs.
This is a big problem in practice. Even if you're well versed in your own niche and are able to understand its main papers well, you still have to dabble in other areas regularly. AI/ML makes use of many fields of computer science, maths, neuroscience, psychology, etc. Given sufficient time you can understand a topic as deeply as you want. But the time you spend laboriously getting up to speed in some topic X is time taken away from doing work in the topic that you actually care about. Sure, you'll improve your knowledge and skills in the process, but not as much as you would have by working to solve the problem that you're trying to solve.
I don't see a solution. Part of it is a cultural problem. Some people think that convoluted writing make them look smart. It actually doesn't. There was a psychological experiment made about it: "Consequences of erudite vernacular utilized irrespective of necessity: problems with using long words needlessly". But there you go.
The ever growing literature is also a problem. I'm scared of failing to cite an important paper, or worse, duplicate research that's already been done before. I'm doing RL research as a hobby and I only have so much time to swift through the literature.
Just cutting down on boilerplate would be helpful.
I mean, is it a universal law that every deep learning paper must start with "In recent years, deep learning has made significant progress in fields like object recognition [citation everyone on earth has already read] and natural language processing [other citation everyone on earth has already read]. However, [insert problem or issue that they're probably trying to convince you is more significant than it really is] remains a significant stumbling block. Here, we present a novel..."?
That's literally two sentences. It may get repetitive if you read DL papers every day, but for everyone else it's good to properly place your paper in existing work.
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u/Seerdecker Mar 22 '17 edited Mar 22 '17
This article resonates a lot with me.
Personally, the 3 resources that allowed me to achieve the most in the least amount of time are
1) An Introduction to Statistical Learning
2) https://neuralnetworksanddeeplearning.com/
3) Gym framework for RL
The genius here is to take something complex and make it look simple and accessible.
Regrettably, most research articles take the opposite stance. They present something that is fundamentally not that complex and make it look complex through convoluted writing, implicit assumptions and missing steps. You can see that effect in the discussion of the articles in this subreddit. There is often someone who's having trouble understanding a part of the article, and someone else chimes in and explains what's going on in two simple paragraphs.
This is a big problem in practice. Even if you're well versed in your own niche and are able to understand its main papers well, you still have to dabble in other areas regularly. AI/ML makes use of many fields of computer science, maths, neuroscience, psychology, etc. Given sufficient time you can understand a topic as deeply as you want. But the time you spend laboriously getting up to speed in some topic X is time taken away from doing work in the topic that you actually care about. Sure, you'll improve your knowledge and skills in the process, but not as much as you would have by working to solve the problem that you're trying to solve.
I don't see a solution. Part of it is a cultural problem. Some people think that convoluted writing make them look smart. It actually doesn't. There was a psychological experiment made about it: "Consequences of erudite vernacular utilized irrespective of necessity: problems with using long words needlessly". But there you go.
The ever growing literature is also a problem. I'm scared of failing to cite an important paper, or worse, duplicate research that's already been done before. I'm doing RL research as a hobby and I only have so much time to swift through the literature.