
Ask HN: Is machine learning worth learning for hobbyists/pet projects? - kace91
I&#x27;m wondering if the field is accessible beyond pure theory for people who doesn&#x27;t have exclusive access to datasets or powerful machines.<p>Can a regular programmer with a macbook achieve something at home? Or would I be stuck doing the same 3&#x2F;4 basic programs like character recognition and hit a wall after the basics?
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fundamental
Are you trying to do anything in particular?

ML can be used in a number of different facets. On one hand you can grab pre-
trained models that people have sitting on github and glue them together to
make working projects for your own use. The other extreme is working through
the theory to create a new method on a dataset you've collected yourself. Most
of the tradeoff is in how much time you want to invest in solving a problem or
learning something new.

The major thing for hobby problems is learning how to best use what's already
out there given your own restrictions on data and computational resources. The
algorithms themselves can be quite complex, but as long as you're not
targeting state of the art results, there should be plenty of tools to help
get yourself started.

My general recommendation is to dive into some of the papers out there and
blog posts on different methods to get a feel for the breadth of options as
well as the cases which may not currently be within your own personal grasp.

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matt_the_bass
I think the best way to move on to the next stage is find a project you want
to do. I find it really hard to just learn a tools if I have no real project
for which to use it.

I don’t know what suits your fancy, but some example projects could be:

\- image recognition: is that your cat at the door or someone else’s?

\- voice recognition: app to add a product to shopping list from a voice
command

\- etc

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tixocloud
It depends on your use case and what you're looking to get out of it. How much
data will you need to crunch? 1 million rows of data should be fairly easy to
crunch through on a personal computer.

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chas
Machine learning is a bigger set of techniques than deep learning. Linear
regression and random forests still work fine and can get good results in many
problem domains without particularly much compute hardware. In addition non-
learned feature engineering can get good results in computer vision if you
constrain your input images.

Do you have particular application areas or problems you want to apply ML to?

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kace91
> Do you have particular application areas or problems you want to apply ML
> to?

Not particularly, no. I've spent a ton of time reading about the math behind
it and I feel as if I had been reading about functional programming or OOP
without actually writing a single line of code... So I'm basically looking to
get the practical approach, getting my hands dirty so to speak.

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CocoaGeek
I'll think it depends on your end goal. Do you want to explore new network
architectures and/or associated functions, or use (your own or public) data
set to explore possible applications?

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rajacombinator
The secret (and possible barrier) to doing something interesting with ML is to
have an interesting dataset. It’s definitely accessible if you’re willing to
put in the effort.

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kace91
Could you be a little more specific about that last part? Do you mean that
interesting datasets are available somehow, or that the field itself is
accessible?

~~~
rajacombinator
Both! But it can take some hard work to acquire interesting data and then
figuring out what to do with it.

~~~
Regardsyjc
Amazon has a great repository of open data. You can go wild with these. [1]
NYC also has tons of open data for their smart cities initiative. [2]

[1][https://registry.opendata.aws/](https://registry.opendata.aws/)
[2][https://opendata.cityofnewyork.us/](https://opendata.cityofnewyork.us/)

