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I have no experience with the entrepreneur/VC side of this coin, but Data Scientists know the differences between traditional regression based ML and CNNs. We k

I have no experience with the entrepreneur/VC side of this coin, but Data Scientists know the differences between traditional regression based ML and CNNs. We know that doing image recognition or NLP is best suited to nvidia/tensorflow/keras/deeplearning/buzzword/buzzword. We also know that SVMs & Random Forests & regression lines work just as well as they always have to make predictions based on your click-thru data. Maybe we should explain the cost/benefit of the buzzwords vs. the science?

pif 10y ago
Your comment sounds wise, but with all those acronyms and technical terms I can't be sure :-)
logfromblammo 10y ago
It's simple. More buzzwords equals more money. So whatever it is you're really doing, just find some way to semi-plausibly attach the latest hot buzzwords to it. Most of the "dinosaurs" around here should know by now that you don't get paid to do what works; you get paid to do what the boss/customer wants. (And that's not always going to be exactly what they asked for, either.) As long as you continue to throw money at me, I'll continue to chase your wild geese, hunt your snipes, fish for your red herrings, and joust with your wind turbines. Most of us here can learn fast enough to always stay one or two steps ahead of the boss on whatever topic they might latch on to. And that becomes genuine experience if it somehow manages to graduate past the business fad phase.
Eridrus 10y ago
To say something in defence of the deep learning religion: it's a very flexible framework for expressing lots of different ideas and the ability to train all those ideas jointly if you can frame them in terms of SGD updates is very appealing. And even if you don't want to use SGD to fit your model, the deep learning machinery can be reused for other things like bayesian inference, eg PyMC3 and Edward are built on Theano/TensorFlow respectively. There have even been papers showing differentiable trees integrated into the deep learning framework. There is work on how to input graphs. Maybe this is just my own desire for unifying frameworks shining through, but joint training seems to have real benefits and feature learning has clearly had a large impact on reinforcement learning. Maybe it's not the best tool for day to day data science work, but that tends towards what tools are accessible and good enough, rather than what is best.
ska 10y ago
It's not even that clean. A lot of the buzzword impact causes mass confusion on the applicability of techniques. Lots of people are trying to apply deep learning techniques on too small amounts of data (and truth) with fairly predictable results ... and throwing around at best questionable performance numbers with little validation. CNN's are great for some image recognition/classification problems, but that's just the start of the conversation.

pif 10y ago

Your comment sounds wise, but with all those acronyms and technical terms I can't be sure :-)

logfromblammo 10y ago

It's simple. More buzzwords equals more money. So whatever it is you're really doing, just find some way to semi-plausibly attach the latest hot buzzwords to it. Most of the "dinosaurs" around here should know by now that you don't get paid to do what works; you get paid to do what the boss/customer wants. (And that's not always going to be exactly what they asked for, either.) As long as you continue to throw money at me, I'll continue to chase your wild geese, hunt your snipes, fish for your red herrings, and joust with your wind turbines. Most of us here can learn fast enough to always stay one or two steps ahead of the boss on whatever topic they might latch on to. And that becomes genuine experience if it somehow manages to graduate past the business fad phase.

Eridrus 10y ago

To say something in defence of the deep learning religion: it's a very flexible framework for expressing lots of different ideas and the ability to train all those ideas jointly if you can frame them in terms of SGD updates is very appealing. And even if you don't want to use SGD to fit your model, the deep learning machinery can be reused for other things like bayesian inference, eg PyMC3 and Edward are built on Theano/TensorFlow respectively. There have even been papers showing differentiable trees integrated into the deep learning framework. There is work on how to input graphs. Maybe this is just my own desire for unifying frameworks shining through, but joint training seems to have real benefits and feature learning has clearly had a large impact on reinforcement learning. Maybe it's not the best tool for day to day data science work, but that tends towards what tools are accessible and good enough, rather than what is best.

ska 10y ago

It's not even that clean. A lot of the buzzword impact causes mass confusion on the applicability of techniques. Lots of people are trying to apply deep learning techniques on too small amounts of data (and truth) with fairly predictable results ... and throwing around at best questionable performance numbers with little validation. CNN's are great for some image recognition/classification problems, but that's just the start of the conversation.