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Learning Machine Learning: A beginner's journey

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Re: Learning Machine Learning: A beginner's journey

#31

Post by SomeStupidPoint »

placebo wrote: post #21
saurabhjha wrote: post #10

I think this "machine learning for hackers" approach is just not enough. Oftentimes, you do need a solid theoretical/mathematical background. Most people seems to approach ML like they approach programming tools or libraries - learn just enough to get job done and move on. I was studying machine learning from Andrew Ng's CS229 (the class videos are online. I think they date from 2008 or hereabout). There is no way yo…

I understand where you're coming from and also agree in principle, but I'd change the claim that "this approach is just not enough" with "this approach is just not enough for achieving many things in machine learning including breaking new ground". I think there's always a way to be creative within the constraints and concepts/axioms you take as given. For example, the fact that I have absolutely no control (or knowl…

I think in both cases -- ML and software in general -- the basics open 80-90% of the field up, and that's enough for most people.

But I'd never do safety critical embedded devices without understanding the physical processor.

And there are similar limits for ML.

Re: Learning Machine Learning: A beginner's journey

#32

Post by jventura »

Philipp__ wrote: post #22

Distributed Systems and ML are probably two most interesting things that I have on the radar, that got me really scared to the point where I do not know from where to start, and most importantly for what?! Most of my free time (time I spent on personal projects) was writing physics simulation in Java, playing with Lisp and doing some backend development. Nothing amazing. Year and a half ago I got really interested in…

Any relevant books, moocs or talks about distributed systems that you or other hn readers can share?

Re: Learning Machine Learning: A beginner's journey

#33

Post by Philipp__ »

jventura wrote: post #32
Philipp__ wrote: post #22

Distributed Systems and ML are probably two most interesting things that I have on the radar, that got me really scared to the point where I do not know from where to start, and most importantly for what?! Most of my free time (time I spent on personal projects) was writing physics simulation in Java, playing with Lisp and doing some backend development. Nothing amazing. Year and a half ago I got really interested in…

Any relevant books, moocs or talks about distributed systems that you or other hn readers can share?

Maybe look here: https://github.com/theanalyst/awesome-distributed-systems

Re: Learning Machine Learning: A beginner's journey

#34

Post by stevehiehn »

saurabhjha wrote: post #10

I think this "machine learning for hackers" approach is just not enough. Oftentimes, you do need a solid theoretical/mathematical background. Most people seems to approach ML like they approach programming tools or libraries - learn just enough to get job done and move on. I was studying machine learning from Andrew Ng's CS229 (the class videos are online. I think they date from 2008 or hereabout). There is no way yo…

Everytime there is a paradigm shift there is always that voice: If you don't understand the paint at a chemical compound level you can't make a beautiful painting. Wait what?

Re: Learning Machine Learning: A beginner's journey

#35

Post by am17an »

saurabhjha wrote: post #10

I think this "machine learning for hackers" approach is just not enough. Oftentimes, you do need a solid theoretical/mathematical background. Most people seems to approach ML like they approach programming tools or libraries - learn just enough to get job done and move on. I was studying machine learning from Andrew Ng's CS229 (the class videos are online. I think they date from 2008 or hereabout). There is no way yo…

> I think a lot of competitive advantage of Google/Facebook in ML is because they have staffed their engineering with people who have studied these things for years (by PhD).

This is not entirely true. Most of their advantage comes from the corpus of data. Of course, I'm not discounting the fact that they're pioneers in the field, but at this stage data is their competitive advantage (hence they open sourced Tensorflow.)

I feel the present state of ML libraries or even distributed system libraries not being a black-box solution and not the "just works" type is a growing pain, and will be evolve into something more accessible/robust in the future. The whole point of it being a "layer of abstraction" is that you don't need to know the details.

Re: Learning Machine Learning: A beginner's journey

#36

Post by cf »

saurabhjha wrote: post #10

I think this "machine learning for hackers" approach is just not enough. Oftentimes, you do need a solid theoretical/mathematical background. Most people seems to approach ML like they approach programming tools or libraries - learn just enough to get job done and move on. I was studying machine learning from Andrew Ng's CS229 (the class videos are online. I think they date from 2008 or hereabout). There is no way yo…

Although to do serious production level ML, I agree that you need to understand the math. But as a starting point, the machine learning for hackers is a great place to start.

I think writing some algorithms and using them to solve problems provides great motivation for the math. In particular, the math will explain why certain approaches did and did not work. Without the hacking that material can get a bit dry.

Re: Learning Machine Learning: A beginner's journey

#37

Post by habitue »

legulere wrote: post #8

A counterpoint: Deep learning is currently hyped, making you not consider other techniques that might work better, or are simpler and work just as good. Deep learning might have a limited scope and turn out to be a dead end for areas other than the ones already examined.

> making you not consider other techniques that might work better

Currently, DL is the most powerful technique for many problem types. I think for a beginner, learning that is a safe bet, the "next thing" is likely to be an elaboration on DL. It's good if some people ignore the hype, they may come up with the next paradigm going in a completely different direction from DL. But the people doing that won't be beginners. They'll be people with similar levels of experience as Yann LeCun, Geoffrey Hinton, etc. Those well rounded people with deep theoretical knowledge will make the big breakthroughs. Beginners should start with what we know works well now, and expand out.

> [Other techniques may be] simpler and work just as good

This is a much better reason to learn other things than DL. Efficiency and lower complexity if you know the problem domain is amenable to the technique.

Re: Learning Machine Learning: A beginner's journey

#38

Post by solipsism »

saurabhjha wrote: post #10

I think this "machine learning for hackers" approach is just not enough. Oftentimes, you do need a solid theoretical/mathematical background. Most people seems to approach ML like they approach programming tools or libraries - learn just enough to get job done and move on. I was studying machine learning from Andrew Ng's CS229 (the class videos are online. I think they date from 2008 or hereabout). There is no way yo…

This is backward thinking. It borders on elitist, although I know it's not meant that way.

Developers everywhere use Paxos without even knowing it, much less having read Lamport's papers, because they're building on top of solid tools that use Paxos (or Raft or what have you). This is more true at Google and Facebook than anywhere.

Same goes for ML. You can study the theory, and you can learn to apply it. In the field's nascency you basically need to understand the theory in order to apply anything, but eventually robust tools are built upon which developers can build systems without having "studied these things for years (by PhD)".

Re: Learning Machine Learning: A beginner's journey

#39

Post by iaw »

Admirable intentions by the author but I hope (s)he changes his font/formatting style.

The current font with dense paragraphs makes it hard for me to read without a headache, sparser sentences (either via bullet pointed lists or illustrative images) are much easier for me to parse.

Re: Learning Machine Learning: A beginner's journey

#40

Post by jupiter90000 »

I have an almost opposite problem. I spent years learning alot of ML stuff and worked at a job doing this kind of work for a couple years or so. I think the issue was that the data we had at the organization and the internal politics seemed to make it difficult to use for ML in a way that mattered to the business. I grew frustrated with having spent alot of time learning things that were exciting then realizing it didn't really matter if some manager can just say "we're doing it this other way that makes sense to me." (Not based on data, but gut feelings)

I'm not sure what to do with that. Probably ML works best in organizations and situations that are on board for using ML to make decisions for the business. Here's the other thing -- finding a business where ML is core to its decision making that will hire a person with no formal ML related education may be difficult. Perhaps I'm wrong about that and have just given up on ML after my frustrating experience.

Now I'm building data systems that the business uses on a daily basis to get things done. I feel alot better doing that than ML stuff, even though I loved playing with data and ML. I guess I've given up on ML for now, maybe I'll find my way back to it again.

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