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0 pointsmlthoughts20187y ago0 comments

You need more than a decade to become proficient with deep learning at the level of researchers solving novel business problems.

It takes at least a decade just to study the prerequisite materials in vector calculus, linear algebra, advanced statistics, classifier algorithms, convex and gradient-based optimization, matrix computations and numerical methods, and associated software engineering skills. That’s all just to get to “base camp” of deep learning.

On the flip side, it’s pretty low effort to just use plug-n-play network components from popular libraries and follow a few tutorials or open source projects.

That’s why there’s effectively zero employment demand for the skill of naive keras or pytorch lego building. It’s as easy as it is meaningless.

Given that you’d already have been spending a decade+ of your life on advanced math if you planned to work on deep learning to solve real problems, there’s a huge impedance mismatch with this idea that you’d somehow also magically just be happy ignoring that specialized skill and the time investment sunk into it to then instead be happy writing throw-away little Flask apps or optimizing routine ETL queries.

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6 comments · 2 top-level

dchichkov7y ago· 2 in thread

My assumption was "starting from a post-graduate level in computer science, natural sciences or equivalent". By the way, I don't see how anyone could have more than a decade specifically in deep learning, considering that the field had started at around that time.

On a flip side, TensorFlow 2.0 and AutoML are coming ;). And generic RL agents that do not require reward hacking are also on the horizon. Who cares, if a researcher spend 10000 hours reading articles AND 10000 hours building products, if a more general algorithm obsoletes it all ;)

mlthoughts2018OP7y ago

> “My assumption was "starting from a post-graduate level in computer science, natural sciences or equivalent".”

Yes, same for me. This builds in nearly a decade of preparatory work into the timeline... so it seems we agree.

> “On a flip side, TensorFlow 2.0 and AutoML are coming ;). And generic RL agents that do not require reward hacking are also on the horizon.”

I work professionally in deep learning for image processing. This quote reads like parody to me. I cannot imagine anyone familiar with the realities of AutoML or deep reinforcement learning talking this way. It’s like an excerpt from the script of Silicon Valley.

dchichkov7y ago

Have you used AutoML in practice for DNN architecture search?

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jonathankoren7y ago· 2 in thread

>You need more than a decade to become proficient with deep learning at the level of researchers solving novel business problems.

No. Even these people haven’t been doing it for a decade.

mlthoughts2018OP7y ago

Yes. They were doing 4 years of math intensive undergrad and 5+ years of math intensive post-grad work before there even was an advent of deep learning in 2008-2012.

jonathankoren7y ago

So they were doing deep learning as an undergrad, huh? Studying tensors for 10 years? Next you’ll be telling me they have 20 years experience coding for CUDA.

No. None of these people have the math background you think, nor do you need it.

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6 comments · 2 top-level

dchichkov7y ago· 2 in thread

mlthoughts2018OP7y ago

> “My assumption was "starting from a post-graduate level in computer science, natural sciences or equivalent".”

Yes, same for me. This builds in nearly a decade of preparatory work into the timeline... so it seems we agree.

> “On a flip side, TensorFlow 2.0 and AutoML are coming ;). And generic RL agents that do not require reward hacking are also on the horizon.”

dchichkov7y ago

Have you used AutoML in practice for DNN architecture search?

1 more reply

jonathankoren7y ago· 2 in thread

>You need more than a decade to become proficient with deep learning at the level of researchers solving novel business problems.

No. Even these people haven’t been doing it for a decade.

mlthoughts2018OP7y ago

Yes. They were doing 4 years of math intensive undergrad and 5+ years of math intensive post-grad work before there even was an advent of deep learning in 2008-2012.

jonathankoren7y ago

So they were doing deep learning as an undergrad, huh? Studying tensors for 10 years? Next you’ll be telling me they have 20 years experience coding for CUDA.

No. None of these people have the math background you think, nor do you need it.

1 more reply

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