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Results tagged with neural-networks
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user 58414
Network structure inspired by simplified models of biological neurons (brain cells). Neural networks are trained to "learn" by supervised and unsupervised techniques, and can be used to solve optimization problems, approximation problems, classify patterns, and combinations thereof.
6
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Difference Between Residual Neural Net and Recurrent Neural Net?
What is the difference between a Residual Neural Net and a Recurrent Neural Net?
As I understand,
Residual Neural Networks are very deep networks that implement 'shortcut' connections across multipl …
1
vote
Large number of layers in a neural network?
To expand on @D.W.♦'s answer.
They are correct in stating
In principle, there is no limit on the number of hidden layers that can be used in an artificial neural network
There are however mul …
- 368
1
vote
When a trained RNN is tested, is the number of time-steps same for every input?
When a trained RNN is tested, is the number of time-steps same for
every input?
Typically, yes. If an input is too short, it can be padded. If an input is too long, it can encoded in some way t …
- 368
6
votes
Accepted
How do neural networks create results like its inputs?
These are known as Autoencoders. As you said, these neural networks are trained to produce output that is similar to the input, rather than output a classification of some kind.
Internally, they do n …
- 368
2
votes
Accepted
How does a recurrent connection in a neural network work?
When a recurrent network is calculated you can imagine the network is 'unrolled' out through time.
When visualized this way, you can see activation and loss can be calculated using the same method …
- 368