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AI Expert Roadmap

i.am.ai

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Re: AI Expert Roadmap

#11

Post by jorlow »

tnbalsam wrote: post #4

Perhaps I may be mistaken, but this seems to be a very long road for a more shallow understanding of deep learning. I'd venture this was written by someone who has a more traditional machine learning background that wants new people to the industry to have that same foundation; however, I'd venture that that is a rather inefficient way to get to deep learning proficiency. If I were to give a recommendation, it would…

> I'd venture this was written by someone who has a more traditional machine learning background that wants new people to the industry to have that same foundation

This is the vibe I got as well. Which is fair enough (to each his/her own), but I thought I'd mention fast.ai which takes the opposite approach:

> Harvard professor David Perkins, who wrote Making Learning Whole (Jossey-Bass), has much to say about teaching. The basic idea is to teach the whole game. That means that if you're teaching baseball, you first take people to a baseball game or get them to play it. You don't teach them how to wind twine to make a baseball from scratch, the physics of a parabola, or the coefficient of friction of a ball on a bat.

Re: AI Expert Roadmap

#12

Post by petr25102018 »

Maybe a better way to attack this problem is to watch Harvard's Introduction to AI course (or similar one) and from there pick an area that piqued your interest. This is because AI is such a vast field that nobody can learn everything (on top of data processing etc.). I made some notes from the ^ course + listed relevant Python libraries to have a good place to start: https://stribny.name/blog/2020/10/artificial-intelligence-in...

Re: AI Expert Roadmap

#15

Post by nmrcq »

With all due to respect to the author of the site, mastering all the materials in the machine learning or data scientist path will make you a solid 'applied' machine learning/statistical learning practitioner, but not an expert, and definitely not a research candidate.

We should be careful with how we guard our scale of semantic meaning - if somebody with an undergrad understanding of statistics (frequentist statistics only in this map) can be called an Expert in a statistical/mathematical field, what do we even call somebody with all the same applied software engineering & exploratory analysis, and business experience, but also a PhD in theoretical topics (math, stats, etc)? A 'super expert'? What do we call Francis Chollet, or LeCun, or anybody else? What's the differentiation between an expert from the roadmap and the team of individuals deploying GPT-3 into Google Assistant? Are they the same?

As a hiring manager and team lead for a large fintech firm in London, I would happily see an individual who had really mastered the above path(s) as a strong candidate for an intermediate or upper junior role in applied data science/machine learning. But ... it's not enough to be a senior, and certainly not an expert. Just my two cents.

Re: AI Expert Roadmap

#16

Post by bdamm »

nmrcq wrote: post #15

With all due to respect to the author of the site, mastering all the materials in the machine learning or data scientist path will make you a solid 'applied' machine learning/statistical learning practitioner, but not an expert, and definitely not a research candidate. We should be careful with how we guard our scale of semantic meaning - if somebody with an undergrad understanding of statistics (frequentist statisti…

Expertise is relative. Having compiled a few kernels in my life I wouldn't consider myself an operating systems expert, but to a career plumber, I am. Heck, a friend of mine is a PhD in physics, operates a particle accelerator for work, and to him I'm an operating systems expert despite my protests to the contrary.

Re: AI Expert Roadmap

#17

Post by mdifrgechd »

Nothing wrong with this roadmap but I'd suggest that this and similar ones are squarely in the "trade" category of formation, where the focus is on a large number of practical topics instead of a more solid grounding in the fundamentals, that mostly dont even concern themselves with practical dat science - what I would call the "university" approach, but maybe not in the sense of a modern university.

For work as a tradesperson, it is worth only focusing on the practical application- and I'm saying this genuinely. But I think it should be clearly distinguished from the different kinds of lasting benefits that a more solid fundamental education provides, including the flexibility to adapt.

I'm very biased here, having studies Electrical Eng and CS before modern ML was remotely mainstream, and comparing my conceptual understanding and what I learned in school about math and linear algebra with the way things are understood by tradespeople with far more knowledge of modern tools than me. So crotchety old person- maybe, but I'm happy I went to university.

Re: AI Expert Roadmap

#18

Post by amaigmbh »

tnbalsam wrote: post #4

Perhaps I may be mistaken, but this seems to be a very long road for a more shallow understanding of deep learning. I'd venture this was written by someone who has a more traditional machine learning background that wants new people to the industry to have that same foundation; however, I'd venture that that is a rather inefficient way to get to deep learning proficiency. If I were to give a recommendation, it would…

You are right that the "Deep Learning" section is rather shallow up to now. We are currently working on expanding it to offer a more comprehensive view of the field and expect to release this update next week. Stay tuned! :)

Re: AI Expert Roadmap

#19

Post by randcraw »

bdamm wrote: post #16
nmrcq wrote: post #15

With all due to respect to the author of the site, mastering all the materials in the machine learning or data scientist path will make you a solid 'applied' machine learning/statistical learning practitioner, but not an expert, and definitely not a research candidate. We should be careful with how we guard our scale of semantic meaning - if somebody with an undergrad understanding of statistics (frequentist statisti…

Expertise is relative. Having compiled a few kernels in my life I wouldn't consider myself an operating systems expert, but to a career plumber, I am. Heck, a friend of mine is a PhD in physics, operates a particle accelerator for work, and to him I'm an operating systems expert despite my protests to the contrary.

You're an expert if other experts say so. The opinions of clueless folk are just random noise.

Just because the man who can see is king among blind men doesn't mean they're qualified to call him visionary.

Re: AI Expert Roadmap

#20

Post by mdifrgechd »

nmrcq wrote: post #15

With all due to respect to the author of the site, mastering all the materials in the machine learning or data scientist path will make you a solid 'applied' machine learning/statistical learning practitioner, but not an expert, and definitely not a research candidate. We should be careful with how we guard our scale of semantic meaning - if somebody with an undergrad understanding of statistics (frequentist statisti…

Agree 100% with you but would add that from the perspective of a lot of business people, the bar for expert is actually pretty low and however far over it you are, they dont actually get any more out or you because they don't know what to do with you. What a lot of businesses want is someone they can call an expert but that still operates within the realm and understanding of a non specialist manager.
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