4 ms·
What are some problems where Deep Learning shines? Does it outperform other algorithms on those problems? Is there an understanding of why? Context: I have som
by jey 13y ago
What are some problems where Deep Learning shines? Does it outperform other algorithms on those problems? Is there an understanding of why?
Context: I have some background in statistical sorts of machine learning algorithms and am genuinely puzzled by this "deep learning" phenomenon and why it's catching on.
- ma2rten 13y agoDeep learning can expose hidden non-linear relationships in the data. It's state-of-the-art in applications such as object recognition from images and voice recognition. There have also been promising results in the natural language processing field. What all of these have in common is that they are "real" artificial intelligence, i.e. teaching computers things that they have been historically bad at compared to humans. Search for a talk by Geoffrey Hinton if you are genuinely interested.
- Houshalter 13y agoDeep learning is the state of the art on image and sound data. It's taking over natural language processing as well. Neural networks work well because they can learn complicated functions from the data. With image, sound and maybe text, the data has structure that can be exploited by NNs. Convolutional neural networks take advantage of this. So instead of learning "pixel 4,773 correlates with the output", we can look at a smaller number of image patches and learn "features" from each patch that correlate with the output. We can go further and create multiple layers of these. Each small image patch extracts some features which are used by the layer above it, and so on to the final output.