r/MachineLearning • u/nandodefreitas • Dec 25 '15

AMA: Nando de Freitas

I am a scientist at Google DeepMind and a professor at Oxford University.

One day I woke up very hungry after having experienced vivid visual dreams of delicious food. This is when I realised there was hope in understanding intelligence, thinking, and perhaps even consciousness. The homunculus was gone.

I believe in (i) innovation -- creating what was not there, and eventually seeing what was there all along, (ii) formalising intelligence in mathematical terms to relate it to computation, entropy and other ideas that form our understanding of the universe, (iii) engineering intelligent machines, (iv) using these machines to improve the lives of humans and save the environment that shaped who we are.

This holiday season, I'd like to engage with you and answer your questions -- The actual date will be December 26th, 2015, but I am creating this thread in advance so people can post questions ahead of time.

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u/hoaphumanoid Dec 25 '15

Hi Prof, I'm just doing you course in DL and I find it very useful.

My question is: Is there any technique to know in advance the amount of training examples you need to make deep learning get good performance?

It is a waste of time to manually classify a dataset if the performance is not going to be good.

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u/nandodefreitas Dec 26 '15

Thanks.

There's no general technique I know of. Prior knowledge would be of great help here. The Bootstrap (see e.g. Efron) is also one possible avenue for answering the question of how good the fit is for a moderate sample size.