Does anybody have resources on the math behind ML? I hit a dead end using Python frameworks because it was a black box, and I simply lacked the underlying knowledge.
Ask HN: Full-on machine learning for 2020, what are the best resources?
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Re: Ask HN: Full-on machine learning for 2020, what are the best resources?
#32Honestly, skip all of the courses. Pick a problem to solve, start googling for common models that are used to solve the problem, then go on github, find code that solves that problem or a similar one. Download the code and start working with it, change it, experiment. All of the theory and such is mostly worthless, its too much to learn from scratch and you will probably use very little of it. There is so much ml cod…
This. Learn top down, not bottom up. Watch maybe one or two short videos on back propagation. You don't need to be muddled in the theory and the math - you can become productive right away. Once you start playing with pytorch and tensorflow models (train them yourself or do transfer learning), you'll start to develop an intuition for how the network graphs fit together. You'll also pick up tools like tensorboard. Als…
> I have no formal training (...) I have real time models that run on the CPU (..) and as far as I know they're more performant than anything else out there
> You do not need to be a data scientist. Anybody can do it. That said, a good GPU will help a lot. I'm using two 1080Ti in SLI and they're pretty decent
An alternative is that, by not knowing what you are doing, you may not see all the options that exist -- and when you hit a problem too hard, you just throw more hardware (GPUs) at it.
This is not to say it is not sometimes a valid approach, but I'd be wary of someone who say hasn't had any formal training in C, and says that his stuff is more performant that anything out there- just because lack of training causes not knowing stuff that already exists.
Re: Ask HN: Full-on machine learning for 2020, what are the best resources?
#33Honestly, skip all of the courses. Pick a problem to solve, start googling for common models that are used to solve the problem, then go on github, find code that solves that problem or a similar one. Download the code and start working with it, change it, experiment. All of the theory and such is mostly worthless, its too much to learn from scratch and you will probably use very little of it. There is so much ml cod…
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Re: Ask HN: Full-on machine learning for 2020, what are the best resources?
#34I started with with the machine learning course[0] on Coursera followed by the deep learning specialization[1]. The former is a bit more theoretical while the latter is more applied. I would recommend both although you could jump straight to the deep learning specialization if you're mostly interested in neural networks.
Re: Ask HN: Full-on machine learning for 2020, what are the best resources?
#35Humblebundle has a bundle of machine learning books right now: https://www.humblebundle.com/books/python-machine-learning-p... I'm considering buying this bundle. Any of these books you would recommend?
Re: Ask HN: Full-on machine learning for 2020, what are the best resources?
#36Paid shill. See this duplicate comment on reddit:
https://old.reddit.com/r/MachineLearning/comments/ei2iz6/d_i...
Re: Ask HN: Full-on machine learning for 2020, what are the best resources?
#37Study calculus, from the definition of real numbers and to taking complex integrals via residuals; then study linear algebra to some theorems about eigenvectors. 1 month total, assuming you're somewhat talented and determined to spend 12 hours a day learning proofs of boring theorems. After that you'll realise that most of the ML papers out there are just ad-hoc composed matrix multiplications with some formulas used as fillers. At that point I think it's more useful to learn what ML models work in practice (although nobody will be able to explain why they work, including the authors) and mix this practical knowledge with the math theory to develop good intuition.
I'd compare ML with weather models: we understand physics driving individual particles, we understand the high level diff equations, but as complexity builds up, we have to resort to intuition to develop at least somewhat working weather models.
Re: Ask HN: Full-on machine learning for 2020, what are the best resources?
#38Does anybody have resources on the math behind ML? I hit a dead end using Python frameworks because it was a black box, and I simply lacked the underlying knowledge.
Week 1-5 of https://www.coursera.org/learn/machine-learning
Re: Ask HN: Full-on machine learning for 2020, what are the best resources?
#39Another suggestion: I like https://spinningup.openai.com for learning reinforcement learning.
Re: Ask HN: Full-on machine learning for 2020, what are the best resources?
#40Honestly, skip all of the courses. Pick a problem to solve, start googling for common models that are used to solve the problem, then go on github, find code that solves that problem or a similar one. Download the code and start working with it, change it, experiment. All of the theory and such is mostly worthless, its too much to learn from scratch and you will probably use very little of it. There is so much ml cod…
true for other fields than AI as well. theory most of the time makes sense at the moment you need it for a practical problem.