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Learning about RBMs and DBNs

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Deep Belief Nets for Python
===========================
`python-dbn` aims to make experimenting with different deep learning 
architectures. Right now, it's capability is limited to specifying deep 
feed-forward networks.

An example:
```python
from dbn        import DBN
from dbn.layers import *

net = DBN([
		OneHotSoftmax(8),
		Sigmoid(3)
	  ],3)

net.fit(train_data,train_labels)
net.predict(test_data)
```

###There's a still a lot left to do
- **Persistence of learnt weights**
  Of highest importance
- **Multiple visible layer sets per RBM**
  Multiple types of input for each RBM. Hopefully this will make
  experimenting different ways for data input to the network possible.
- **Auto-encoders**
  Building on to the DBN class to create an intuitive way to
  build auto-encoders just by specifying the layer dimensions.
- **Documentation**
  I'd like the library to be as thoroughly documented as scikit-learn.
  I've learnt a ton from that library, largely because the documentation
  of the library is so complete, going right down to the theory level of
  things, just enough so you can quickly understand and start implementing.
  My hope is that this library achieves similar goals.

###Getting the damn theory right
I'm still realy new at this, so I'd appreciate any help I can get with the 
theory side of things.
Things I'm still unsure of that are already implemented:
- When do I stop training for RBMs? I've looked at Hinton's tutorial on
  training RBMs, but it doesn't seem to be helping for some of the tasks
  I've tested it with.
- With a softmax layer one-hot input, should my reconstruction be also be 
  turning just one neuron on based in the probability densities? Or should
  I just leave the values as probabilities? I've heard (though I can't
  remember from where) that this results in slower learning due to the more
  distributed nature of the updates.
- Learning rate decay with RBMs: Yay or nay?

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