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Ask HN: In 2022, what is the proper way to get into machine/deep learning?

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Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#21

Post by pbowyer »

nonrandomstring wrote: post #16

You could do worse than go back and make sure your foundation maths is solid. Revise some Discrete Maths books and understand identification, classification, sets, equivalence... Make sure you've a solid ground on concepts like dimensions, functions, differentiation, integration, extrapolation, interpolation, then toughen up your Linear Algebra, optimisation, solving, regression, before getting into approximation and…

> You could do worse than go back and make sure your foundation maths is solid.

This. Though I have no textbook I'd recommend; all of the ones I used were a very hard slog to read, let alone grasp the maths in them.

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#22

Post by santiagobasulto »

Can I suggest a longer, but (I think) better route?

Try the Data/ML Engineer route. Instead of going directly into ML, try to work as a “supporter” of those doing ML. There’s a HUGE gap there, specially if you’re a good programmer.

There are a lot of people in the “pure” ML space, people with science background, with phDs, etc.

But there’s not enough people to support them: taking their models to producing, building their pipelines, etc.

If you get into Data/ML engineer, you’ll be working with these people and learning from them.

It’s a longer route for sure, but I think it can yield the highest success rate.

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#23

Post by k_sze »

If you want a good theoretical foundation based on maths, Caltech's Learning From Data course is good: https://work.caltech.edu/telecourse

You need to be not afraid of doing proofs of theorems (most of them have to do with stats because machine learning is basically stats on steroids).

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#24

Post by brw95 »

As always, it depends.

I started out without *any* background knowledge a few years ago. Found the Data Scientist career track of Datacamp pretty helpful, since it goes beyond programming and includes the mathematical and statistical theories as well. (https://www.datacamp.com/tracks/data-scientist-with-python)

It's basic, but a solid foundation to build upon.

If you're already familiar with most of these topics, fast.ai is the way to go!

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#25

Post by code51 »

santiagobasulto wrote: post #22

Can I suggest a longer, but (I think) better route? Try the Data/ML Engineer route. Instead of going directly into ML, try to work as a “supporter” of those doing ML. There’s a HUGE gap there, specially if you’re a good programmer. There are a lot of people in the “pure” ML space, people with science background, with phDs, etc. But there’s not enough people to support them: taking their models to producing, building…

The trouble is that these positions try to filter you out asking for the same concepts in interviews. Nobody cares for how good a programmer you are. Data engineer positions don't work well if you want to touch cutting-edge concepts like big-scale DNNs. Sure you'd learn about deploying things but if your day-to-day job is not about e.g. ONNX, pytorch, JAX and massive scale training/inference pipelines, then you'd still be left out.

Chicken and egg problem. Probably the optimal way is just aiming for top ML research teams right from the start as a new grad. No other way is better.

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#26

Post by throwaway81523 »

Don't know about 2022, but in 2020 the fast.ai video series was a good way to get started. It has been revised a few times since then, so chances are it is still good.

If you want something more theoretical there is a book by Hopcroft et al. that was released in draft form a number of years ago. It appears to be out for real now: Foundations of Data Science, by Avrim Blum, John Hopcroft, and Ravindran Kannan. Blurb and video lectures: https://www.microsoft.com/en-us/research/publication/foundat... I just found these so haven't looked at them yet. The book draft (2014, wow) is here: https://www.cs.cornell.edu/jeh/book11April2014.pdf I didn't stick with it long enough to make much progress, unfortunately.

Kaggle.ai problems are a good set of practical projects even if you're not aiming to be competitive at them (which takes a lot of effort and resources). The Fast.ai vids are ok as preparation for them.

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#28

Post by jstx1 »

santiagobasulto wrote: post #22

Can I suggest a longer, but (I think) better route? Try the Data/ML Engineer route. Instead of going directly into ML, try to work as a “supporter” of those doing ML. There’s a HUGE gap there, specially if you’re a good programmer. There are a lot of people in the “pure” ML space, people with science background, with phDs, etc. But there’s not enough people to support them: taking their models to producing, building…

Don't group data engineer and ML engineer together - they're very different positions. Data engineers typically don't do any ML (in fact teams often create DE positions to differentiate between the people who do ML and the people who don't); for ML engineers is varies depending on the team/company - some work more like applied data scientists, others focus almost entirely on infrastructure and deployment.

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#29

Post by dejv »

A lot of people here recommend fast.ai which is solid, but not really that useful if you want to do research.

I would start with math foundations: basic linear algebra, stats, probability and some analysis. CS undergrad level is plenty of math for start.

Then I would try to understand back prop on intimate level: learn how to calculate gradients, maybe take a look on how autograd works as well.

Then you should know a bit to pick your next steps by yourself.

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#30

Post by wokwokwok »

santiagobasulto wrote: post #22

Can I suggest a longer, but (I think) better route? Try the Data/ML Engineer route. Instead of going directly into ML, try to work as a “supporter” of those doing ML. There’s a HUGE gap there, specially if you’re a good programmer. There are a lot of people in the “pure” ML space, people with science background, with phDs, etc. But there’s not enough people to support them: taking their models to producing, building…

> I think it can yield the highest success rate.

At what?

If you want to be an SRE for a data platform, sure, but this pretty thankless work:

- cleaning up dodgy data

- cleaning up behind low code data pipelines and other painful integration work with systems that suck and sometimes just don’t work (like PowerBI).

- cleaning up behind data scientists that create models in arcane and imaginative ways and expect you to “productionise” them.

- cleaning up behind brain dead scheduling systems that fail unexpectedly.

- constant churn with partners and cloud products for whatever the latest hotness is.

If you want to be solving actual problems with ML, this is a dead end.

It’s IT support for the people doing real work.

…so, it depends on your goals. Getting a job? Sure! Everyone wants a workhorse who they can dump all the annoying problematic on-call tasks to.

Learning ML, contributing to research, building models?

This isn’t a path that leads there.

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