Optimizing a Week of Machine Learning Learning

post by Raemon · 2018-01-09T06:55:10.168Z · score: 25 (6 votes) · LW · GW · 2 comments

Contents

  Some plausible goals
  Time Allotted
  Possible Actions
    i. Textbook Reading
    ii. Practical Practice
    iii. Reading Papers 
  Questions I notice that'd help
    Employability of ML 
    How tractable is "stay up to date on AI developments", with a goal of AI Safety? 
None
2 comments

status: rough braindump. doesn't really attempt to communicate my background assumptions.

tldr: help Raemon optimize a week of Machine Learning reading/practice, with an eye for relevance towards understanding and/or helping with AI safety

I'd like to spend some time in January learning some cursory stuff about machine learning.

I currently know "the layman's gist" of what Machine Learning, Reinforcement Learning and Neural Networks mean, but nothing about how to apply them.

My coding background is in web development.

I have no math bathground.

Some plausible goals

(I'm not actually sure which of these should be highest priority, listed approximately in the order the occurred to me)

Goal 1 Gain some literacy in ML stuff for purposes of understanding technological developments relevant to AI safety. Subgoals:

Goal 2: I know of at least a few positions currently for "ML technician that assists a researcher" (the ones I know of are AI safety related, and I have an impression that non-AI-Safety positions might exist as well, or in the future). I want to explore whether this is a plausible thing I might become competent enough at to be hireable.

Depending on how the field of Machine Learning is shaped, this might be something I want to do just to keep up with the world in general for career purposes, or it might be a set of skills useful for projects I care about.

Goal 3: After a brief bit of reading, it became clear this would involve re-acquainting myself with mathematical notation, which currently makes my eyes glaze over. It seemed like re-training that skill might at least make it possible to skim papers and have a rough idea of what they say, and this might be useful even if I don't pursue this professionally.

Time Allotted

Right now I don't have huge amounts of time for this, but will be dedicating the third week of January to putting as much effort into it as I can. (Realistically I'm guessing 5 hours a day of serious effort). Prior to 3rd week of January I'll be doing a few 1-hour sessions of work in the evening

So, goal is to optimize approximately 40 hours total of learning, mostly aiming for "explore value" to verify if I should spend additional time later.

Possible Actions

Things that easily occur to me are:

i. Textbook Reading

ii. Practical Practice

iii. Reading Papers

Questions I notice that'd help

Underlying:

Employability of ML

How tractable is "stay up to date on AI developments", with a goal of AI Safety?

Motivational questions - what sorts of projects could I do that'd feel like I accomplished something? (I'm a big fan of using projects to learn things, although also a big fan of tutorials giving you superpowers. dunno)

2 comments

Comments sorted by top scores.

comment by Gyrodiot · 2018-01-09T09:16:03.237Z · score: 26 (6 votes) · LW · GW

Hi Raemon! This is a topic I'm very bad ad writing structured answers about, and much better at chatting about, because there are tons of things to say and I'd need more details to know how to steer my advice.

That being said, I recommend you this repository for resources, aimed at people with a tech, but not necessarily math, background. Reading though some of the guides there should help you solve some of your last-section questions.

I'd say that staying up to date on AI developments with a goal of AI safety is entirely tractable as long as you're not looking for the particular techniques that will lead to unsafe AI. Most AI literature is entirely disconnected from AI safety concerns, and if you dive into the field enough, you will become proficient enough to understand the papers that are relevant to safety concerns.

Cute little ML projects almost always have hidden depths, if you're dealing with real-world data. I suggest to try them after tutorials, not as tutorials, so that you'll be able to split whatever you're trying to do in manageable chunks (and understand why things fail or succeed).

I wish you the best for your endeavor!

comment by Raemon · 2018-01-09T23:09:58.550Z · score: 5 (1 votes) · LW · GW

Thanks! That repository looks helpful.