I studied post-grad ML and I can say you really don't need to go this deep to use ML - check out http://fast.ai for a code-first practical crash course :)

The author fails to mention this, but this is meant to be a cookbook for machine learning research.

The discussion 3 months ago on "Part 2" mentioned a re-launch on the "Part 1" content ("redoing the whole of part 1 starting in October"); how's that working out? I wish the tedious maintenance (keeping instructions updated with the latest versions of the software, etc.) was a higher priority there.

https://news.ycombinator.com/item?id=14887414

Apparently there's been a bit of "broken links and poorly organized content". If you decide to try it out, don't miss http://wiki.fast.ai/

PS. Ask HN: Best way to get started with AI? | https://news.ycombinator.com/item?id=15689399 (3 days ago)

As someone more at home in statistics than ML/CS, it's funny that I find this easier to understand than the more 'true' cookbook style postings. The real 'grok' for me would be this kind of mathematical intuition, combined with real data examples. I always learn by just trying to recreate models in books, down to the algebra. If you can feel it and inspect it, you'll get it.

I really appreciate his sharing his notes that translate fairly complex math into something like an intuition. It fits in a helpful (but sparsely populated!) niche between academic papers and tensorflow-for-software-engineers tutorials.

I must admit I did not understand even the basics of this posting. I Need to start way further behind this level of abstraction.

Yep. Second formulae and I'm out of my league. Is it just me that gets frustrated when things aren't explained? What do I have to study to be able to read this

θ∗,ψ∗←argminθ,ψg(θ,ψ)

With no prior explanation and say, yep, now that you put it that way, it's true?

Notation is notation. I think it's hard to come up with a single guide because a lot of conventions depend on the area.

In the context of optimization, a star refers to the value of the variable that minimizes (or maximizes) a function you're interested in.

You will also see a hat over a variable (https://en.wikipedia.org/wiki/Hat_operator#Estimated_value) sometimes (from statistics) which is an estimate of some quantity from data (and that estimate may be obtained by minimizing a function - solving an optimization problem)

By the way, this is the first result when googling argmin: https://en.wikipedia.org/wiki/Arg_max

This isn't a formula that is 'true', it's a minimally specified program.

It takes a function g, and returns the values of it's arguments (theta and psi) that minimize the function g.

This might help https://en.wikipedia.org/wiki/List_of_mathematical_symbols

Idk about you guys but this is not what I think of when I hear the word "cookboook"

I would love to learn more about machine learning as a web developer, but simply put it's a different industry with entirely different constructs.

I found a great article[1] that at least helped me build a simplistic understanding as a modern developer.

[1]. https://simpleprogrammer.com/2017/10/02/machine-learning-mod...

It depends on what you're cooking. All that a cookbook really implies is a template recipe to apply when you are faced with a certain situation.

But yeah...It's definitely not a beginner ML cookbook.