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-# Feed-Forward Neural Network (FFNN) for Regression Problems
+# Signal Filtering
 
 ## Reference
 
-- Mathematical background: ["Neural Networks and Deep Learning"](http://neuralnetworksanddeeplearning.com/index.html).
-
-- Datasets: [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets.php).
-
 ## Characteristics
 
-- The code has been written and tested in Python 3.7.7.
-- Usage: *python test.py example*.
-
 ## Parameters
 
-`example` Name of the example to run (wine, stock, wifi, pulsar.)
-
-`problem` Defines the type of problem. Equal to C specifies logistic regression, anything else specifies linear regression. The default value is `None`.
-
 ## Examples
-
-There are four examples in *test.py*: wine, stock, wifi, pulsar. Since GDO is used, `use_grad` is set to `True`. For all examples `init_weights` is also set to `True`.
-
-### Single-label linear regression examples: wine
-
-```python
-data_file = 'wine_dataset.csv'
-n_features = 11
-hidden_layers = [20]
-split_factor = 0.7
-L2 = 0.0
-epochs = 50000
-alpha = 0.2
-d_alpha = 1.0
-tolX = 1.e-7
-tolF = 1.e-7
-```
-
-Original dataset: <https://archive.ics.uci.edu/ml/datasets/Wine+Quality>.
-
-The dataset has 11 features, 1 label, 4898 samples, 261 variables, and a layout of [11, 20, 1].
-
-Correlation predicted/actual values: 0.708 (training), 0.601 (test).
-
-Exit on `epochs` with `tolX` = 2.0e-4 and `tolF` = 1.1e-7.