Ask HN: How can a front-end developer dive into machine learning?

Find a company with a data-science department, which needs engineers to industrialize their proof-of-concepts (POCs). The POCs might often include front-end work. Once you are in, learn machine-learning on the side and try to transition into a machine-learning role.

EDIT: On top of it, if software engineering is your strength (testing, automatic deployment, etc), data-scientists will also benefit, since you can show them how to professionally develop a sw product.

Different approaches suit different people and PHD is a relatively specialized route. It's good to have people targeting similar goals with different approaches.

For an anecdote, I recall hearing one of the Kaggle founders mention that many of their bounties are won by non-statisticians/ML-ists. Producing novel (in the academic sense) stuff is unlikely outside of an academic setting, but producing products or solving problems is do-able.

Edit/comment: no need to downvote rsrsrs86 people. He's putting forward a position and defending it, not trolling. If you disagree, then disagree. The whole point of a thread like this is hearing people's take. Surely, PHD is a valid suggestion.

Hey honest question.

I have worked with ai and nlp guys. How i have seen this works out: there is a problem x. They get the best most recent respected research on the problem x. They implement it most of the time it's on (github).

If it doesn't solve the problem at hand they shrug their shoulders and say something like "it is the standford nlp parser can't do better than that!"

the concept "getting into ai" - I am confused. We need more people to git clone ai repos? Or are these people truly interested in ai research - at that point they should be looking at a phd.

Then people pile on: "learn how an nn works!" Uh why? Anyone can git clone and setup nodes. I am missing something. Please help.

Yes, to actually improve the state of the art, you start a PhD, I agree.

But there's still also a lot of work that people can do applying the "Github repositories" to new problems. And to do that effectively, you also have to know stuff (e.g. you need to be able to read the most recent research, now when tool X is appropriate over tool Y, know what preprocessing makes sense in a given situation, etc). There's money to be made there and people want to do that work.

Kaggle competitions are very restricted in the sense that they are supervised learning problems. This typically results in applications in analytics. This should o.k. be easier to get into.

But ML can do much more than analytics., and much more than supervised problems. And the great problems to be solved are not supervised problems. They involve learning as you go, without a clean database with examples to learn from. They are adaptive problems.

You might optimize prices in an online retail player by trying to estimate supply and demand curves, but you will fail, and the best way to do it is not much different than teaching a neural network to play video games, but is fundamentally different from supervised learning and regressions.

ML can do self-driving cars, it can build drones that learn to fly, it can translate horses to zebras, it can play defeat humans at Go, it can make guitars sound like pianos.

There is a lot of technique and theory into framing any problem as a problem that can be solved by machine learning. Machine learning is generally not feasible unless you restrict the problem properly.