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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?

#111

Post by [deleted] »

ChrisRackauckas wrote: post #72
bigmit37 wrote: post #56

Is C/C++ still worth learning if o want to create some models from scratch (new layers or different paradigms) I hear that C++ is a nightmare to work with and was wondering if Rust,Julia, or even Swift would be worth learning instead. I know Python but deep learning frameworks seem to be written in C++, so to come up with new layers I need to understand C++, which I was told has lot of peculiarities that takes time t…

Julia is a blast to do research on this stuff in, if you want to go beyond the basics like TensorFlow and PyTorch allows. The 2020's is going to be the decade of mixing numerical PDEs with machine learning IMO, and Julia already has a lot of features along these lines that are missing from "traditional ML" libraries.

[deleted]

Re: Ask HN: Full-on machine learning for 2020, what are the best resources?

#112

Post by hakmad »

thosakwe wrote: post #31

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.

Mathematics for Machine Learning - https://mml-book.github.io/

Re: Ask HN: Full-on machine learning for 2020, what are the best resources?

#113

Post by forgingahead »

I had a nice experience with Adam Geitgey's Machine Learning is Fun course.

He published a lot of free ML blog posts, in easy-to-understand writing with nice examples, so it never made anything seem out-of-reach. I found that a lot of other material was a little too abstract, so his stuff was great.

The blog posts are here: https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec...

And I also bought his paid course with code samples -- it's affordable and good value.

Re: Ask HN: Full-on machine learning for 2020, what are the best resources?

#114

Post by woodson »

echelon wrote: post #51
1996 wrote: post #32

Earlier quoted context omitted.

> 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…

> 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. Maybe some will. I just explained that I'm running my models on CPUs, so I'm actually developing sparse and efficient resource constrained models that evaluate quickly. I've been working with libtorch's JIT engine in Rust (tch.rs bin…

Are you working on VC (input: speech of one speaker, output: the same spoken content, but sounds like another speaker) or speaker-adaptive speech synthesis (input: text, output: speech)?

Also check out ParallelWaveGAN, another high-quality and very fast (on CPU) neural vocoder.

Re: Ask HN: Full-on machine learning for 2020, what are the best resources?

#116

Post by stared »

martingoodson wrote: post #105
codingslave wrote: post #6

Honestly, 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…

>Also, I will throw in my consiracy theory that most ML researchers and such push the theory/deep stats requirement as a form of gatekeeping. Learning the fundamentals of a field is supposed to be gatekeeping. It's what stops you from making stupid mistakes. The field of ML is littered with horrible errors made by people who don't know the fundamentals. Please don't follow this terrible advice.

You can say that about any field, discipline or skill.

At the same time, there is a difference whether one starts learning that, and one wants to apply it in a large, production system with social implications (be it advertising, medicine, or anything). Hobby projects, or even small startups, rarely fall in that region.

Moreover, even a profound knowledge of mathematics does not give any edge in ethics, or even - awareness of problems with real data (noise, bias, malicious use, social reception, etc).

Re: Ask HN: Full-on machine learning for 2020, what are the best resources?

#117

Post by username90 »

rramadass wrote: post #109
cameronbrown wrote: post #107

Earlier quoted context omitted.

Doesn't it depend on what you're trying to do? I think there's a huge difference between research and learning enough to scrap something together for a hobby project. The deep maths can come later. I don't need to study compiler theory to use GCC.

Your analogy is wrong i.e. you are comparing apples to oranges. ML is very different from other "normal" computation systems. * Non-ML: Input + {Rules} = Output * ML: Input + Output = {Rules} where "{Rules}" = Infinite set of possible "Programs" each of which is a trace through a very large state space of variables. In the first case, we humans use all our ingenuity to write the program and tweak it to get the right…

> How in the world do you even know that your encodings are correct?

Easy, you know that they aren't and will ever be entirely correct for complex enough ML problems, just like humans. The ways to handle its errors is not an ML topic though, you just have to ensure via old fashioned system design that the system you build doesn't depend on any ML model to always output correct results.

Re: Ask HN: Full-on machine learning for 2020, what are the best resources?

#118

Post by lordgrenville »

jamesxv7 wrote: post #63

There is a question I have been asking for quite some time. It is known that Python is the language of choice when practicing ML. But, can similar results be achieved using Powershell? What makes Python superior to Powershell when making models for ML?

Wow, I would never think to use Powershell outside of some Windows-specific tinkering. I guess every language has its diehard fans.

Re: Ask HN: Full-on machine learning for 2020, what are the best resources?

#120

Post by methusala8 »

fxtentacle wrote: post #100
codingslave wrote: post #6

Honestly, 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…

If you want to raise your salary from $10 to $20 per hour, playing with existing models is the way to go. If you want to make serious money solving real problems, take the time to learn about automated differentiation and all the related mathematics about how gradients flow backwards through the network. But like the coding slave (great nick BTW) said, first play a bit, then learn how it works. Image transformation G…

Awesome answer. Any particular resources that you can point to in order to learn this?
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