diff --git a/TelescopeML/StatVisAnalyzer.py b/TelescopeML/StatVisAnalyzer.py
index 378ea8dd..863f0bb0 100644
--- a/TelescopeML/StatVisAnalyzer.py
+++ b/TelescopeML/StatVisAnalyzer.py
@@ -176,7 +176,7 @@ def regression_report(trained_model,
 
         f.tight_layout()
         target_name = ['Gravity', 'C_O_ratio', 'Metallicity', 'Temperature'][i]
-        plt.savefig(f'../outputs/figures/regression_report_{target_name}.pdf', format='pdf')
+        # plt.savefig(f'../outputs/figures/regression_report_{target_name}.pdf', format='pdf')
         plt.show()
 
 def filter_dataset_range(dataset, filter_params):
@@ -537,7 +537,7 @@ def boxplot_hist(data,
         x_label = 'c_o_ratio'
     if x_label == '[M/H]':
         x_label = 'metallicity'
-    plt.savefig(f'../outputs/figures/boxplot_hist_{x_label}.pdf', format='pdf')
+    # plt.savefig(f'../outputs/figures/boxplot_hist_{x_label}.pdf', format='pdf')
 
     plt.show()
 
@@ -1396,7 +1396,7 @@ def plot_filtered_dataframe(dataset, filter_bounds, feature_to_plot, title_label
     dict_features = {'temperature': 'T$_{eff}$ [K]', 'gravity': 'log$g$', 'metallicity': '[M/H]', 'c_o_ratio': 'C/O'}
     cbar.set_label(dict_features[feature_to_plot])
 
-    plt.savefig(os.path.join(__reference_data__, 'figures', feature_to_plot + "_trainin_examples.pdf"), dpi=500,
-                bbox_inches='tight')
+    # plt.savefig(os.path.join(__reference_data__, 'figures', feature_to_plot + "_trainin_examples.pdf"), dpi=500,
+    #             bbox_inches='tight')
 
     plt.show()
diff --git a/docs/conf.py b/docs/conf.py
index 23bd8968..967ac626 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -97,8 +97,8 @@
 }
 
 
-#nbsphinx_prolog = """
-#{% set docname = env.doc2path(env.docname, base=None) %}
-#.. note::  `Download full notebook here <https://github.com/EhsanGharibNezhad/TelescopeML/tree/master/docs/{{ docname }}>`_
-#"""
-###.. only:: html
+nbsphinx_prolog = """
+{% set docname = env.doc2path(env.docname, base=None) %}
+.. note::  `Download full notebook here <https://github.com/EhsanGharibNezhad/TelescopeML/tree/master/docs/{{ docname }}>`_
+"""
+##.. only:: html
diff --git a/docs/index.rst b/docs/index.rst
index 4aa2f4f2..1b577ad1 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -33,7 +33,7 @@ or simply...
 ======================
 
 .. toctree::
-   :maxdepth: 1
+   :maxdepth: 2
    :hidden:
   
    Installation <installation>
diff --git a/docs/knowledgebase.rst b/docs/knowledgebase.rst
index a9603152..d35ae241 100644
--- a/docs/knowledgebase.rst
+++ b/docs/knowledgebase.rst
@@ -1,9 +1,3 @@
 KnowledgeBase
 ===============
 
-ML Concepts: Data processing and Model Training in CNN
--------------------------------------------------------
-.. toctree::
-   :maxdepth: 2
-
-    Neural Network Arch  <tutorials/Concepts_ML_knowledge_Train_CNN_Model.ipynb>
diff --git a/docs/tutorials.rst b/docs/tutorials.rst
index f7900072..9b7ca8bb 100644
--- a/docs/tutorials.rst
+++ b/docs/tutorials.rst
@@ -1,21 +1,22 @@
 Tutorials
 ==========
 
+
 Exploring the Datasets
 -----------------------
 .. toctree::
-   :maxdepth: 3
+   :maxdepth: 1
    
     Brown Dwarf Synthetic Dataset  <tutorials/1__BrownDwarf_Data_Exploration.ipynb>
 
-   
+
 
 
 
 Predict Atmospheric Parameters
 -------------------------------
 .. toctree::
-   :maxdepth: 3
+   :maxdepth: 2
 
    Deploy CNN Model to Predict Brown Dwarf Atmospheric Parameters  <tutorials/2__Deploy_ML_Models_Predict_BrownDwarf_Parameters.ipynb>
 
@@ -24,14 +25,14 @@ Predict Atmospheric Parameters
 
 
 
+
 Train a Regression ML Model
 ----------------------------
 .. toctree::
-   :maxdepth: 3
+   :maxdepth: 2
 
    Train a Regression ConvNN Model using the BOHB Tuned Hyperparameters  <tutorials/3__Train_CNN_Model.ipynb>
 
 
 
 
-