Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Ask HN: Tutorials written with heavy dependencies

news.ycombinator.com

Post reply on HN
Print view View original

1–10 of 53 posts

Ask HN: Tutorials written with heavy dependencies

#1

Post by bilekas »

I am quite stubborn in a lot of ways but one in particular is when I'm guiding a team member on something I like to explain the workings so that there is a high level understanding of how things are working.

In the last couple years, I've been dipping my toes into other areas for 'hobby' time and wanting to know how things I use & like work.

A great example is the machine learning: An immediate google gets you as far as 'install these 10x libs' then write this.

When you dig into the OS of those libraries they're overwhelming and the documentation is never focused on the underlying functionality, which I personally am giddy to learn.

I find myself having to go with trial and error, I hate this because the wheel has already been invented. Maybe I'm missing a resource.

It feels these tutorials are just tutorials in libraries.

I know source code IS the workings, is there a resource other than source code I'm missing ?

Re: Ask HN: Tutorials written with heavy dependencies

#2

Post by pixel_tracing »

This may not be a sexy idea but one example I can thing of Tom Mitchell’s Machine Learning book, it gives the bare bones of what machine learning is (albeit it is missing some of the newer architectures) but that gives a great overview of how to build these architectures from scratch

Re: Ask HN: Tutorials written with heavy dependencies

#5

Post by bilekas »

tmtvl wrote: post #3

There is a source you may be missing: the human factor. Try talking to people involved with the project and ask them for any insights they're willing to share.

Absolutely 100%, I go to any meetups of conferences that are around in my areas. And I have friends who are with a similar mentality of me trying to figure it out.

I maybe should have rephrased the question.

Re: Ask HN: Tutorials written with heavy dependencies

#6

Post by bilekas »

henning wrote: post #4

what about Data Science from Scratch, Deep Learning from Scratch, and similar books where they just use numpy or something and do everything from mostly first principles?

Nice options, as a hobby and just dipping my toes, I like to not pay for anything before I know if I like it or not. Saying that, I did buy a book on PLC's while I was just starting because I had one to play with.. Fundamentals of Programmable Logic Controllers. It felt like playing a video game before googling the answers.

Loved every min.

Re: Ask HN: Tutorials written with heavy dependencies

#8

Post by bilekas »

pixel_tracing wrote: post #2

This may not be a sexy idea but one example I can thing of Tom Mitchell’s Machine Learning book, it gives the bare bones of what machine learning is (albeit it is missing some of the newer architectures) but that gives a great overview of how to build these architectures from scratch

> Machine Learning: A multistrategy approach added to cart

Thanks

Re: Ask HN: Tutorials written with heavy dependencies

#9

Post by ChrisMarshallNY »

I remember attending a meetup, once, where it was supposed to be a tutorial on GraphQL. I was barely familiar with GraphQL, when I went in, and left, almost exactly the same.

The tutorial was actually all about a couple of JS libraries that you could use as GraphQL abstractions. I am a Swift programmer, and was a lot more interested in the actual GraphQL interface, which was barely mentioned.

Lot of that stuff, going around...

Re: Ask HN: Tutorials written with heavy dependencies

#10

Post by steeps »

Machine learning is an enormous field, and if you are after a explanation/exploration from the ground up, then Kevin Murphy's Probabilistic Machine Learning: An Introduction is very good, but a bit of a tome.

If instead, you want to focus on neural networks, I found Michael Nielsen's Neural Networks and Deep Learning an excellent resource for implementing them from first principles (available at http://neuralnetworksanddeeplearning.com/).

Post reply on HN