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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?

#51

Post by echelon »

1996 wrote: post #32
echelon wrote: post #8

Earlier quoted context omitted.

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…

> 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 bindings).

I'm currently trying to adapt Melgan to the Voice Conversion problem domain so I can get real time, high-fidelity VC without using a classical vocoder. WORLD works great and quickly, but it's a poor substitute for the real thing as it only maps the fundamental frequency, spectral envelope, and aperiodicity. Melgan is super high quality and faaast.

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

#52

Post by aliveupstairs »

lettergram wrote: post #4

I’d suggest: https://fast.ai - good intro on practical neural networks. I wrote a guide to ML based NLP. We identify if a sentence is a question, statement or command using neural networks: https://github.com/lettergram/sentence-classification The truth is you don’t need to understand all the math right away with neural networks. Mostly it’s getting an understanding of why you use a given layer, bias, etc and when. O…

https://www.fast.ai/

https://fast.ai is unsafe.

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

#53

Post by e_ameisen »

A lot of the resources proposed in the comments focus on theoretical knowledge, or a particular sub-domain (Reinforcement Learning, or Deep Learning). I recommend a top down approach where you pick a project and learn by building it. This can be easier said than done however, and after mentoring dozens of junior Data Scientists I wrote a how-to guide for people interested in using ML for practical topics.

You can find it from O'Reilly here (http://shop.oreilly.com/product/0636920215912.do) or on Amazon here (https://www.amazon.com/Building-Machine-Learning-Powered-App...).

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

#54

Post by DrNuke »

> where should I start? I work mostly as a data analyst on pharma where the focus is batch process.

Any tool needs an applied field but any applied field does not need all the tools. You have an applied field already (pharma), so start looking for one or two state-of-the-art ML papers for that? Happy 2020 and good luck, it’s going to be fun!

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

#55

Post by mkl »

jamesxv7 wrote: post #50

I'm impressed by the responses generated in this conversation. My expectation was to get several links and start browsing each one of them. However, many have agreed that the best way is to start with a specific example and start creating a model. Many times I have tried to answer that same question, "which model to apply"? How do I know I'm not re-inventing the wheel?

> How do I know I'm not re-inventing the wheel?

You probably are, but for learning purposes that doesn't matter at all.

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

#56

Post by bigmit37 »

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 to pick up. Compiler isn’t also very user friendly (what I’ve read)

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

#57

Post by tensor »

codingslave wrote: post #13
kartoshechka wrote: post #10

Earlier quoted context omitted.

I hope you're trolling because this is a guaranteed way to climb a peak of stupidity [1]. If OP is determined to get a bit deeper than 30 min guides on Medium, there is sure theory to learn. But it is merely second year of college, and probably you would like to skip Kolmogorov axiomatics and measure theory, it won't hurt your understanding of bleeding edge researches. [1] https://en.m.wikipedia.org/wiki/Dunning%E2%8…

My opinion is that the theory starts to make sense after you know how to use the models and have seen different models produce different results. Very few people can read about bias variance trade off and in the course of using a model, understand how to take that concept and directly apply it to the problem they are solving. In retrospect, they can look back and understand the outcomes. Also, most theory is useless…

What usually happens is that people get something working, think they now know ML, but don't even generally know enough to understand the things they did wrong, and never end up getting to the theory.

The best approach is to learn both concurrently. Learn some theory, apply it and understand that applications including pitfalls, then learn a bit more and repeat. Incremental learning with a solid base. It's fun to hate on academia but this is how experts with deep knowledge of a domain get to where they are.

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

#58

Post by mkl »

vjktyu wrote: post #37

Study 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…

What are complex integrals used for in machine learning?

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

#59

Post by zyl1n »

Be sure to check out 3Blue1Brown's linear algebra series as well. (Maybe after you've built your own MNIST network) Blew my mind when I made the connection that each layer in a dense NN is learning how to do a linear transformation + a non-linear "activation" function.

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

#60

Post by jamesxv7 »

eachro wrote: post #24

I think it depends on what you want to focus on. If you want to do deep learning, fast.ai is probably the best resource available. Jeremy Howard and Rachel Thomas (the two founders) have poured quite a lot into fostering a positive, supportive community around fast.ai which really does add quite a lot of value. If you want to really understand the fundamentals of machine learning (deep learning is just one subset of…

Excellent recommendation. I really appreciate all the recommendations proposed. Happy New Year eachro.
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