Implements:
-neural networks regression
-binary and multinomial classification
-autoencoders
Several features available for a more efficient gradient descent:

1. mini-batch GD
2. optimization algorithms: 'Adam', 'RMSProp', 'Momentum'
3. Batch normalization
4. Ridge regulrization

Calling Function:"neuralNet"

Graphics: The library also comes with a visualizer (neuralNet_visualizer sub-folder)

1. visualize model summary (error rate, gradient check values, ...)
2. plot objective function for validation and training
3. plot weights evolution through network layers

NN parameters are very flexible:

1. Type= "Classification" or "Regression"
2. DW (Depth/Width of the network)=vector where each value indicates neurons in indexed layer
3. loss (loss function)= "RSS" or "Deviance" (either logistic loss or residual sum of squares)
4. outputFunc (output function)= "Sigmoid", "Softmax", "Identity"
5. activationFunc (activation function)= "tanh", "sigmoid", "linear" (relu on the way)
6. rate=learning rate
7. weightDecay=TRUE/FALSE
8. lambda=coefficient of the weight decay
9. a host of <NN cheking tools such as gradient checker, traceObjective function, trace loss function, ...

1. set type="Regression"
2. set loss="RSS"
3. if input is 0...1 then set output to "Softmax" or "Sigmoid" if parameters >1 or ==1 respectively
4. Other parameters are tuned identically to other neural networks
   Remark: For runtime optimization reasons, the purpose of this implementation is experimentation and is not intended to be used in production environment.
   Please send all your remarks to [email protected]