[D] Staying on top of the DL field?

Submitted by Which-Distance1384 t3_10mr0nj in deeplearning

Hi,

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I was an MLE from 2016-2018, then I became a quant for around 4 years. I was able to land a research ML job recently. But man, has stuff changed in the past 4 years. I think I lag behind many more recent research topics.

If I may ask, what is the most optimum way to get back on track? A degree (probably dont have time due to family responsibilities)? Maybe some professional degree? Or just a few good github repo to clone and play with.

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Please share any thought and ideas you have. Thanks.

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perfopt t1_j66x6kz wrote

I work in the DL area with previous background in systems. The field is diverse and rapidly changing. I have sort of come to the conclusion that - for me - it is best to learn the application of the technology in a few chosen domains (say NLP, image recognition) rather than chasing everything that is happening.

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BellyDancerUrgot t1_j67p0e9 wrote

A degree , maybe a part time professional masters perhaps from a school where the faculty who teaches does active research. Or just read papers. 2 min papers is a good channel to start off with.

In ML/DL 1 year is already ancient. 4 years is prehistoric lol. For context if u choose a topic, say 2D-3D translation , from the advent of NERFs a couple of years back? We have a stupid amount of papers on the topic trying various novel approaches , everything ranging from using Voxels to store geometry in new ways, to geometry aware GANs , multi view compression using ViTs etc etc

So choose a topic and focus on that otherwise it’s a lot.

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Screend t1_j67ybnp wrote

In a somewhat similar boat to you as I’m stepping into a team grappling with problems different to the ones I’ve been specialising in. I would say identifying the most burning topics for your new role, take a short course and most importantly, apply what you’ve learnt on a mini project. Be kind to yourself and pick 2-3 things max that will unlock you in your new role, then build from there.

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