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Results tagged with deep-neural-networks
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user 10
For questions related to deep neural networks, which are artificial neural networks with "many" layers, where "many" can vary depending on the context.
8
votes
Accepted
What is the "dropout" technique?
Dropout means that every individual data point is only used to fit a random subset of the neurons. This is done to make the neural network more like an ensemble model.
That is, just as a random fores …
- 4,282
3
votes
What is the purpose of the hidden layers?
Hidden layers by themselves aren't useful. If you had hidden layers that were linear, the end result would still be a linear function of the inputs, and so you could collapse an arbitrary number of li …
- 4,282
5
votes
Is the pattern recognition capability of CNNs limited to image processing?
The simple answer is "no, they aren't limited to images": CNNs are also being used for natural language processing. (See here for an introduction.)
I haven't seen them applied to graphical data yet, …
- 4,282
5
votes
What is a deep neural network?
Deep networks have two main differences with 'normal' networks.
The first is that computational power and training datasets have grown immensely, meaning that it's practical to run larger networks an …
- 4,282
3
votes
What's the difference between hyperbolic tangent and sigmoid neurons?
I don't think it makes sense to decide activation functions based on desired properties of the output; you can easily insert a calibration step that maps the 'neural network score' to whatever units y …
- 4,282