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

AFAIK FastAI courses are well recommended for their Deep Learning stuff but they also have ML course[0] Another usual recommendation is Elements of Statistical Learning book. Another option is finding a MOOC that you enjoy and following it.

[0]http://course18.fast.ai/ml

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. Once you get some intuition then I’d learn the math.

That’s at least how I instruct others. In any case, there are lots of guides for any flavor. I’d start with deep learning and focus on the “practical” then move to the “theoretical”.

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 code on github to learn from, its really the best way. When you encounter a concept you need to understand, google the concept and learn the background info. This will give you a highly applied and intuitive understanding of solving ml problems, but you will have large gaps. Which is fine, unless you are going in for job interviews.

Also bear in mind that courses like fast.ai (as you see plastered on here), aggresively market themselves by answering questions all over the internet. Its a form of SEO.

EDIT (Adding this here to explain my point better):

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 in the application of ML, and only useful in the active research of new machine learning methods and paradigms. Courses make the mistake of mixing in that useless information.

The same thing is true of the million different optimizers for neural networks. Why different ones work better in different cases is something you would learn when trying to squeeze out performance on a neural network. Who here is intelligent enough to read a bunch about SGD and optimization theory (Adam etc), understand the implications, and then use different optimizers in different situations? No one.

I'm much better off having a mediocre NN, googling, "How to improve my VGG image model accuracy", and then finding out that I should tweak learning rates. Then I google learning rate, read a bit, try it on my model. Rinse and repeat.

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. Modern deep learning results are extremely thin when it comes to theoretical backing.

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.

Also, do transfer learning. It's so awesome to train on a publicly-available high quality and large data set, train for a lot of epochs for good problem domain fit, then swap out your own smaller data set. It's magical.

I have a feeling that ML in the future will be like engineering today. You can learn by doing and don't need a degree or formal background to be productive and eventually design your own networks.

I have no formal training (save one undergrad course that was way outdated in "general AI"), and I've designed my own TTS and voice conversion networks. I have real time models that run on the CPU for both of these, and as far as I know they're more performant than anything else out there (on CPU).

Eventually you might start reading papers. (You'll be productive long before you need to do this.) Most ML papers are open access, but review (broad survey) articles might need pirating. Thankfully there are websites that can help you get these. The papers aren't hard to read if you've spent some time playing with the networks they pertain to. Read the summary, abstract, and figures before diving into the paper. It may take a few reads and some googling.

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.

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%80%93Kruger_effec...