text: Title: provide a way to use deep neural networks
  Body: ### Description
  
  Igel is built on top of sklearn at the moment. Therefore, All sklearn models can be used. This includes of course the neural network models integrated in sklearn (MLP classifier and MLP regressor). However, sklearn is not powerful enough when it comes to deep neural networks. 
  Therefore, this issue aims to include support for using deep neural networks in igel. Maybe Keras API?
  
  ### Example:
  
  ```
  model:
       type: classification # or regression
      algorithm: neural network   # this is already implemented. However, it is using the sklearn NN implementation
      arguments: default   # this will use the default argument of the NN class
  ```
  As you can see. The user can provide these configs in the yaml file and igel will train a neural network. However, the NN model in sklearn is not as powerful as other frameworks like keras, tensorflow, torch etc..
  
  What I mean with this issue and want to implement in the future is **maybe** something like this (feel free to bring new ideas):
  
  ```
  model:
       deep: true
       type: classification # or regression
      algorithm: neural network   # this will now use the Keras NN model since the user added deep: true 
      arguments: default 
  ```
  
  OR Maybe even this can be implemented as a VISION ( This will probably take a long time to implement):
  
  ```
  model:
       uses: keras  # the user can here provide keras, tensorflow or torch 
       type: classification # or regression
      algorithm: neural network   # this will now use the Keras NN model since the user provided that he wants to use keras
      arguments: default 
  ```
  