.

Author: “jake”

exploratory.rst

@@ -94,6 +94,20 @@ Using the 50 percentile to compare among different classes, it is easy to find f
 can have high prediction importance if they do not overlap. Also can be use for outlier detection.
 Features have to be **continuous**.
 
+From different dataframes, displaying the same feature.
+
+.. code:: python
+
+    df = pd.DataFrame({'normal': normal['Pressure'], 's1': cf6['Pressure'], 's2': cf12['Pressure'], 
+                        's3': cf20['Pressure'], 's4': cf30['Pressure'],'s5': cf45['Pressure']})
+    df.boxplot(figsize=(10,5));
+
+.. image:: images/box3.png
+    :scale: 50 %
+    :align: center
+
+From same dataframe with of a feature split by different y-labels
+
 .. code:: python
 
     plt.figure(figsize=(7, 5))

images/box3.PNG

@@ -53,6 +53,37 @@ Model architecture can also be displayed in a graph. Or we can print as a summar
 
     model summary printout
 
+Activation Functions
+----------------------
+
+Hidden Layers
+***************
+
+ReLu (Rectified Linear units) is very popular compared to the now mostly obsolete sigmoid & tanh functions because it
+avoids vanishing gradient problem and has faster convergence. However, ReLu can only be used in hidden layers.
+Also, some gradients can be fragile during training and can die. 
+It can cause a weight update which will makes it never activate on any data point again. Simply saying that ReLu could result in Dead Neurons.
+
+To fix this problem another modification was introduced called Leaky ReLu to fix the problem of dying neurons. 
+It introduces a small slope to keep the updates alive.
+We then have another variant made form both ReLu and Leaky ReLu called Maxout function .
+
+.. figure:: images/deep-activation.png
+    :width: 500px
+    :align: center
+
+    https://towardsdatascience.com/activation-functions-and-its-types-which-is-better-a9a5310cc8f
+
+
+
+Output Layer
+************
+
+Sigmoid: Binary Classification
+Softmax: Multi-Class Classification
+
+
+
 ANN
 -----------