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```
Unify the feedforward, hopfield and som into a single solution. Use a
single neural network type, and different layer types. This will allow
"hybrid" neural networks to be created.
```
Original…
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```
Unify the feedforward, hopfield and som into a single solution. Use a
single neural network type, and different layer types. This will allow
"hybrid" neural networks to be created.
```
Original…
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layers.py, on which most of the nntools code is based, has always been geared towards feed-forward neural networks. We should look into recurrent neural networks as well. Personally I don't have a lot…
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```
Unify the feedforward, hopfield and som into a single solution. Use a
single neural network type, and different layer types. This will allow
"hybrid" neural networks to be created.
```
Original…
-
```
Unify the feedforward, hopfield and som into a single solution. Use a
single neural network type, and different layer types. This will allow
"hybrid" neural networks to be created.
```
Original…
-
```
Unify the feedforward, hopfield and som into a single solution. Use a
single neural network type, and different layer types. This will allow
"hybrid" neural networks to be created.
```
Original…
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Since reversePush is not tail recursive, it results in a stack overflow for even simple networks when you push in a lot of data/use a bunch of memory. Here's the code I used to produce this (backprop …
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```
Unify the feedforward, hopfield and som into a single solution. Use a
single neural network type, and different layer types. This will allow
"hybrid" neural networks to be created.
```
Original…
-
```
Unify the feedforward, hopfield and som into a single solution. Use a
single neural network type, and different layer types. This will allow
"hybrid" neural networks to be created.
```
Original…
-
```
Unify the feedforward, hopfield and som into a single solution. Use a
single neural network type, and different layer types. This will allow
"hybrid" neural networks to be created.
```
Original…