11–20 of 21 posts
Is this really a good idea? I thought neural networks didn't do as well as other methods outside of image classification and problems like it. Many of the advancements in Deep Learning were learning to extract features from unlabeled data. I assume all of Netflix's data is labelled. EDIT: The deaded comment below me makes a good point, but they still have labels in the sense of what everyone watched and for how long.…
Is this really a good idea? I thought neural networks didn't do as well as other methods outside of image classification and problems like it. Many of the advancements in Deep Learning were learning to extract features from unlabeled data. I assume all of Netflix's data is labelled. EDIT: The deaded comment below me makes a good point, but they still have labels in the sense of what everyone watched and for how long.…
Probably not. They'll have a vast amount of information about what people watched, for how long, etc. but they won't have labelled scores for everything. In this case, you'd maybe run an unsupervised algorithm to extract features first (the deep learning bit) then another algorithm to map those features to labels (here you'd use the labelled data).
They may also want to do similarity measurements between different films, in which case again they don't have labelled data (you want to discover the clusters).
While I'm sure they have problems where they do have nice labelled data, I'm sure they have a vast amount of problems where they don't have some or any labels.
Is this really a good idea? I thought neural networks didn't do as well as other methods outside of image classification and problems like it. Many of the advancements in Deep Learning were learning to extract features from unlabeled data. I assume all of Netflix's data is labelled. EDIT: The deaded comment below me makes a good point, but they still have labels in the sense of what everyone watched and for how long.…
Is this really a good idea? I thought neural networks didn't do as well as other methods outside of image classification and problems like it. Many of the advancements in Deep Learning were learning to extract features from unlabeled data. I assume all of Netflix's data is labelled. EDIT: The deaded comment below me makes a good point, but they still have labels in the sense of what everyone watched and for how long.…
Many of the recent headline results for deep learning have been supervised such as the image net classification challenge (http://www.image-net.org/challenges/LSVRC/2013/)
Sadly, the Recommendation Engine is only of limited use...and I hate to be one of those naysayers as I understand the economics and contractual issues here...the movies that Netflix thinks I would really enjoy and are available for streaming and I haven't seen (I watch a lot of movies but am by no means prolific) goes downhill after a couple dozen. It used to be that on the iPad, at least two bottom rows were dedicat…
I wish Netflix would just give me a list of genres I could browse through and stop with the oh-my-so-clever recommendation engine. Maybe I want to stretch my viewing habits... how am I going to see what is on offer if it is always filtered through what I chosen before?
Is this really a good idea? I thought neural networks didn't do as well as other methods outside of image classification and problems like it. Many of the advancements in Deep Learning were learning to extract features from unlabeled data. I assume all of Netflix's data is labelled. EDIT: The deaded comment below me makes a good point, but they still have labels in the sense of what everyone watched and for how long.…
It's actually the multiple layers hidden units that perform non-linear feature extraction and the unsupervised pre-training is simply a means to do this better (theoretically, although we don't really know what's happening as much as it would seem).
Most of the current research shows deep neural nets to be state of the art in image classification and nlp. I don't know that it is the case that deep learning techniques do not work out side this area, it's just there hasn't been much published on it either way. Although I do believe the Kaggle Merck contest was neither of these, and deep learning out performed all other techniques http://blog.kaggle.com/2012/11/01/deep-learning-how-i-did-it...
Isn't this infringing the recent patent of Neurala? http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=H...