- objects in an image/video
- phonemes in raw audio
- events in multidimensional time series
- grammatical/semantic structures in unstructured text
- strategic features in a board game position (e.g. Go)
These kinds of inputs are difficult to handle with traditional ML techniques. But if your data already looks more like rows in a table, with simple, semantically meaningful features, deep learning isn't likely to buy you that much.
On the other hand, deep nets probably won't outperform more traditional machine learning algorithms on less high dimensional, uncompressed data. This problem is exacerbated by the amount of tuning needed by deep nets. Architectures have been fine tuned for common data types such as images and speech, but if the dataset doesn't fall into one of these categories, you have to tune the algorithms yourself.
These links may be helpful: http://deeplearning4j.org/accuracy.html http://deeplearning4j.org/use_cases.html http://deeplearning4j.org/word2vec.html
My experience is that dl method is not as smart as a real human. you can't simply put data in and wait.
the trainer has to understand the data first. and then he needs to choose the best model for the data. sometimes it is black magic. sometimes you need to do many experiments, and in the end, you can't explain why this or that gives the best result. lots of manual work is involved.
but I should say I'm not that experienced with dl.
paper (worth reading): http://arxiv.org/pdf/1506.05869v2.pdf
There are also lots of examples beyond classification - structured prediction such as image segmentation [4], predicting depth, surface normals, and pixels from only RGB [5], text to text translation [6], image captioning [7], Turing machines [8], speech synthesis [9], question answering (Q/A) [10][11], handwriting generation [12], playing Go [13], playing Atari [14], speech recognition [15], drawing things [16] and many more.
[1] http://blog.kaggle.com/2015/07/27/taxi-trajectory-winners-in...
[2] http://blog.kaggle.com/2012/11/01/deep-learning-how-i-did-it...
[3] http://arxiv.org/abs/1403.6652
[4] http://arxiv.org/abs/1502.03240
[5] http://arxiv.org/abs/1411.4734
[6] http://arxiv.org/abs/1409.0473
[7] http://arxiv.org/abs/1502.03044
[8] http://arxiv.org/abs/1410.5401
[9] http://arxiv.org/abs/1506.02216
[10] http://arxiv.org/abs/1507.04808
[11] http://arxiv.org/abs/1503.08895
[12] http://arxiv.org/abs/1308.0850
[13] http://arxiv.org/abs/1412.3409
[14] https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf
[15] http://www.sciencedirect.com/science/article/pii/S0893608014...
I don't do any image-related tasks, but I use machine learning extensively and have never been able to successfully break away from my big three of Naive-Bayes, SVMs, and Random Forests. I've tried some of the more established deep learning techniques (LeNet,RBM,etc), but can't seem to find anything that works better than one of those three. Is deep learning an exclusively image-related technique?