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

And there are similar limits for ML.

I think what Andrew Ng would say is that without a rigorous statistical background, you will be limited in your ability to use ML, and you will certainly be more liable to blow your foot off by using it improperly. That being said, in a subset of cases, you may be able to achieve non-trivial insights through the techniques he teaches in the course.

So how I would rephrase your assertion is that a hacker can probably get a lot more out of ML techniques if they are willing to learn the math underlying them.

I tried doing the ML course without any prior knowledge of Linear Algebra and dropped out after the first three weeks. In hindsight, I realized it wouldn't have been possible to appreciate how PCA works without understanding eigenvectors, how collaborative filtering is an elegant application of matrix factorization and so on.

But after I completed Strang's Linear Algebra course, the entire ML class was a breeze.

Frankly I regret spending time understanding all the math, instead of working for some company, munging through their data and applying some black box stuff. The hype is quite bad IMO.

If you don't work on your fundamentals, you end up simply memorising the algorithms and basic applications. Much like the neurons discussed for deep learning, we need to create rich relationships between our own to ensure we retain and can apply the knowledge in the long term, which starts with laying a foundation of fundamentals.

For brushing up on those fundamentals, I recommend Bertsekas & Tsitsiklis : Introduction to Probability. Theory supported with lots of examples, as well as comparing the theory to "intuition" and why it is much more effective to apply the theory.

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.

A great resource specifically tailored to those that don't have a especially strong grasp of probability and statistics is Grokking Deep Learning

https://iamtrask.github.io/2016/08/17/grokking-deep-learning...

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)".

For ML, I don't think we are at the eventually point just yet.

Anyone who wants to be the guy/gal who understands the fundamentals will be valuable. But we don't need everyone trying to be that person. And most wouldn't be successful, though they'd be successful as the guy/gal who does other stuff.

> 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've been coming across this on HN front page and it's worrysome to an extent.

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.

Not entirely true. Google & FB have orders of magnitude more data than flipkart. You can have the smartest ML people on the planet churning out the most clever, advanced ML algos & models, but without enough data, its not going to be useful & effective.

I recently attended slashn[0], flipkart's annual technical conference, and spoke to a bunch of their ML folks. They have masters & phd degrees in ML from IITs, IISc and are as smart as they come.

Sure, flipkart doesnt have marquee names of the likes of yann lecun, andrew ng, but i wouldnt doubt the ML talent Flipkart has

0. https://slashn.flipkart.net/ ps : I dont work for flipkart, but have friends who work in ML teams

But 99.9% of people are not trying to compete with Google or Facebook. They are looking for basic insights.

I do agree that ML isn't and shouldn't be "easy". But we inevitably and thankfully get abstracted away from the inner workings of things over time.