Merge pull request #102 from jrzaurin/3rd_party_integration_example

3rd party integration example

examples/notebooks/01_Preprocessors_and_utils.ipynb → examples/notebooks/01_preprocessors_and_utils.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##  Processors and Utils\n",
+    "#  Processors and Utils\n",
     "\n",
     "Description of the main tools and utilities that one needs to prepare the data for a `WideDeep` model constructor. \n",
     "\n",
@@ -745,7 +745,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.7 ('base')",
    "language": "python",
    "name": "python3"
   },
@@ -759,7 +759,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "3b99005fd577fa40f3cce433b2b92303885900e634b2b5344c07c59d06c8792d"
+   }
   }
  },
  "nbformat": 4,

examples/notebooks/02_model_components.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Model Components\n",
+    "# Model Components\n",
     "\n",
     "The main components of a `WideDeep` (i.e. Multimodal) model are tabular data, text and images, which are feed into the model via so called `wide`, `deeptabular`, `deeptext` and `deepimage` model components"
    ]
@@ -584,7 +584,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.7 ('base')",
    "language": "python",
    "name": "python3"
   },
@@ -598,7 +598,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "3b99005fd577fa40f3cce433b2b92303885900e634b2b5344c07c59d06c8792d"
+   }
   }
  },
  "nbformat": 4,

mkdocs/sources/examples/03_Binary_Classification_with_Defaults.ipynb → examples/notebooks/03_binary_classification_with_defaults.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Simple Binary Classification with defaults\n",
+    "# Simple Binary Classification with defaults\n",
     "\n",
     "In this notebook we will train a Wide and Deep model and simply a \"Deep\" model using the well known adult dataset"
    ]
@@ -1197,7 +1197,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.7 ('base')",
    "language": "python",
    "name": "python3"
   },
@@ -1211,7 +1211,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "3b99005fd577fa40f3cce433b2b92303885900e634b2b5344c07c59d06c8792d"
+   }
   }
  },
  "nbformat": 4,

examples/notebooks/04_regression_with_images_and_text.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Regression with Images and Text\n",
+    "# Regression with Images and Text\n",
     "\n",
     "In this notebook we will go through a series of examples on how to combine all Wide & Deep components.\n",
     "\n",
@@ -752,7 +752,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.7 ('base')",
    "language": "python",
    "name": "python3"
   },
@@ -766,7 +766,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "3b99005fd577fa40f3cce433b2b92303885900e634b2b5344c07c59d06c8792d"
+   }
   }
  },
  "nbformat": 4,

examples/notebooks/05_save_and_load_model_and_artifacts.ipynb

@@ -4,6 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Save and load model and artifacts\n",
     "In this notebook I will show the different options to save and load a model, as well as some additional objects produced during training. \n",
     "\n",
     "On a given day, you train a model..."
@@ -1113,7 +1114,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.7 ('base')",
    "language": "python",
    "name": "python3"
   },
@@ -1127,7 +1128,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "3b99005fd577fa40f3cce433b2b92303885900e634b2b5344c07c59d06c8792d"
+   }
   }
  },
  "nbformat": 4,

examples/notebooks/06_fineTune_and_warmup.ipynb → examples/notebooks/06_finetune_and_warmup.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## The FineTune/Warm Up option\n",
+    "# The FineTune/Warm Up option\n",
     "\n",
     "Let's place ourselves in two possible scenarios. \n",
     "\n",
@@ -1581,7 +1581,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.7 ('base')",
    "language": "python",
    "name": "python3"
   },
@@ -1595,7 +1595,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "3b99005fd577fa40f3cce433b2b92303885900e634b2b5344c07c59d06c8792d"
+   }
   }
  },
  "nbformat": 4,

mkdocs/sources/examples/07_Custom_Components.ipynb → examples/notebooks/07_custom_components.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Custom components\n",
+    "# Custom components\n",
     "\n",
     "As I mentioned earlier in the example notebooks, and also in the `README`, it is possible to customise almost every component in `pytorch-widedeep`.\n",
     "\n",

examples/notebooks/09_extracting_embeddings.ipynb

 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extracting embeddings\n",
+    "\n",
+    "This notebook is a simple guide to extracting learned feature embeddings using Tab2Vec"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -494,7 +503,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.7 ('base')",
    "language": "python",
    "name": "python3"
   },
@@ -508,7 +517,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "3b99005fd577fa40f3cce433b2b92303885900e634b2b5344c07c59d06c8792d"
+   }
   }
  },
  "nbformat": 4,

examples/notebooks/11_auc_multiclass.ipynb

@@ -431,7 +431,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.7"
   },
   "toc": {
    "base_numbering": 1,

examples/notebooks/12_ZILNLoss_origkeras_vs_pytorch_multimodal.ipynb → examples/notebooks/12_ZILNLoss_origkeras_vs_pytorch_widedeep.ipynb

@@ -4,9 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# [DISCLAIMER]\n",
+    "# ZILNLoss\n",
     "\n",
-    "Purpose of this notebook is to check if ZILNloss [implemented originaly Keras](https://github.com/google/lifetime_value/blob/master/notebooks/kdd_cup_98/regression.ipynb) give same results in pytorch-multimodal implemenatation"
+    "**[DISCLAIMER]**\n",
+    "\n",
+    "Purpose of this notebook is to check if ZILNloss [implemented originaly Keras](https://github.com/google/lifetime_value/blob/master/notebooks/kdd_cup_98/regression.ipynb) give same results in pytorch-widedeep implemenatation"
    ]
   },
   {
@@ -1210,7 +1212,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.7 ('base')",
    "language": "python",
    "name": "python3"
   },
@@ -1224,7 +1226,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.7"
   },
   "toc": {
    "base_numbering": 1,
@@ -1238,6 +1240,11 @@
    "toc_position": {},
    "toc_section_display": true,
    "toc_window_display": false
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "3b99005fd577fa40f3cce433b2b92303885900e634b2b5344c07c59d06c8792d"
+   }
   }
  },
  "nbformat": 4,

mkdocs/sources/examples/13_Model_Uncertainty_prediction.ipynb → examples/notebooks/13_model_uncertainty_prediction.ipynb

@@ -5,7 +5,11 @@
    "id": "57216283",
    "metadata": {},
    "source": [
-    "# Custom DataLoader for Imbalanced dataset"
+    "# Model Uncertainty prediction \n",
+    "\n",
+    "**Note**:\n",
+    "\n",
+    "This notebook extends the \"Custom DataLoader for Imbalanced dataset\" notebook"
    ]
   },
   {

examples/notebooks/14_bayesian_models.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## The Bayesian Models\n",
+    "# The Bayesian Models\n",
     "\n",
     "Perhaps one of the most interesting functionality in the library is the access to full Bayesian models in almost exactly the same way one would use any of the other models in the library. \n",
     "\n",

mkdocs/mkdocs.yml

@@ -36,18 +36,20 @@ nav:
         - Bayesian Trainer: pytorch-widedeep/bayesian_trainer.md
         - Tab2Vec: pytorch-widedeep/tab2vec.md
     - Examples:
-        - 01_Preprocessors_and_utils: examples/01_Preprocessors_and_utils.ipynb
+        - 00_airbnb_data_preprocessing: examples/00_airbnb_data_preprocessing.ipynb
+        - 01_preprocessors_and_utils: examples/01_preprocessors_and_utils.ipynb
         - 02_model_components: examples/02_model_components.ipynb
-        - 03_Binary_Classification_with_Defaults: examples/03_Binary_Classification_with_Defaults.ipynb
+        - 03_binary_classification_with_defaults: examples/03_binary_classification_with_defaults.ipynb
         - 04_regression_with_images_and_text: examples/04_regression_with_images_and_text.ipynb
         - 05_save_and_load_model_and_artifacts: examples/05_save_and_load_model_and_artifacts.ipynb
-        - 06_fineTune_and_warmup: examples/06_fineTune_and_warmup.ipynb
-        - 07_Custom_Components: examples/07_Custom_Components.ipynb
+        - 06_finetune_and_warmup: examples/06_finetune_and_warmup.ipynb
+        - 07_custom_components: examples/07_custom_components.ipynb
         - 08_custom_dataLoader_imbalanced_dataset: examples/08_custom_dataLoader_imbalanced_dataset.ipynb
         - 09_extracting_embeddings: examples/09_extracting_embeddings.ipynb
+        - 10_3rd_party_integration-RayTune_WnB: examples/10_3rd_party_integration-RayTune_WnB.ipynb
         - 11_auc_multiclass: examples/11_auc_multiclass.ipynb
-        - 12_ZILNLoss_origkeras_vs_pytorch_multimodal: examples/12_ZILNLoss_origkeras_vs_pytorch_multimodal.ipynb
-        - 13_Model_Uncertainty_prediction: examples/13_Model_Uncertainty_prediction.ipynb
+        - 12_ZILNLoss_origkeras_vs_pytorch_widedeep: examples/12_ZILNLoss_origkeras_vs_pytorch_widedeep.ipynb
+        - 13_model_uncertainty_prediction: examples/13_model_uncertainty_prediction.ipynb
         - 14_bayesian_models: examples/14_bayesian_models.ipynb
         - 15_DIR-LDS_and_FDS: examples/15_DIR-LDS_and_FDS.ipynb
     - Contributing: contributing.md

mkdocs/sources/examples/01_Preprocessors_and_utils.ipynb → mkdocs/sources/examples/01_preprocessors_and_utils.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##  Processors and Utils\n",
+    "#  Processors and Utils\n",
     "\n",
     "Description of the main tools and utilities that one needs to prepare the data for a `WideDeep` model constructor. \n",
     "\n",
@@ -745,7 +745,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.7 ('base')",
    "language": "python",
    "name": "python3"
   },
@@ -759,7 +759,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "3b99005fd577fa40f3cce433b2b92303885900e634b2b5344c07c59d06c8792d"
+   }
   }
  },
  "nbformat": 4,

mkdocs/sources/examples/02_model_components.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Model Components\n",
+    "# Model Components\n",
     "\n",
     "The main components of a `WideDeep` (i.e. Multimodal) model are tabular data, text and images, which are feed into the model via so called `wide`, `deeptabular`, `deeptext` and `deepimage` model components"
    ]
@@ -584,7 +584,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.7 ('base')",
    "language": "python",
    "name": "python3"
   },
@@ -598,7 +598,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "3b99005fd577fa40f3cce433b2b92303885900e634b2b5344c07c59d06c8792d"
+   }
   }
  },
  "nbformat": 4,

examples/notebooks/03_Binary_Classification_with_Defaults.ipynb → mkdocs/sources/examples/03_binary_classification_with_defaults.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Simple Binary Classification with defaults\n",
+    "# Simple Binary Classification with defaults\n",
     "\n",
     "In this notebook we will train a Wide and Deep model and simply a \"Deep\" model using the well known adult dataset"
    ]
@@ -1197,7 +1197,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.7 ('base')",
    "language": "python",
    "name": "python3"
   },
@@ -1211,7 +1211,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "3b99005fd577fa40f3cce433b2b92303885900e634b2b5344c07c59d06c8792d"
+   }
   }
  },
  "nbformat": 4,

mkdocs/sources/examples/04_regression_with_images_and_text.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Regression with Images and Text\n",
+    "# Regression with Images and Text\n",
     "\n",
     "In this notebook we will go through a series of examples on how to combine all Wide & Deep components.\n",
     "\n",
@@ -752,7 +752,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.7 ('base')",
    "language": "python",
    "name": "python3"
   },
@@ -766,7 +766,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "3b99005fd577fa40f3cce433b2b92303885900e634b2b5344c07c59d06c8792d"
+   }
   }
  },
  "nbformat": 4,

mkdocs/sources/examples/05_save_and_load_model_and_artifacts.ipynb

@@ -4,6 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Save and load model and artifacts\n",
     "In this notebook I will show the different options to save and load a model, as well as some additional objects produced during training. \n",
     "\n",
     "On a given day, you train a model..."
@@ -1113,7 +1114,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.7 ('base')",
    "language": "python",
    "name": "python3"
   },
@@ -1127,7 +1128,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "3b99005fd577fa40f3cce433b2b92303885900e634b2b5344c07c59d06c8792d"
+   }
   }
  },
  "nbformat": 4,

mkdocs/sources/examples/06_fineTune_and_warmup.ipynb → mkdocs/sources/examples/06_finetune_and_warmup.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## The FineTune/Warm Up option\n",
+    "# The FineTune/Warm Up option\n",
     "\n",
     "Let's place ourselves in two possible scenarios. \n",
     "\n",
@@ -1581,7 +1581,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.7 ('base')",
    "language": "python",
    "name": "python3"
   },
@@ -1595,7 +1595,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "3b99005fd577fa40f3cce433b2b92303885900e634b2b5344c07c59d06c8792d"
+   }
   }
  },
  "nbformat": 4,

examples/notebooks/07_Custom_Components.ipynb → mkdocs/sources/examples/07_custom_components.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Custom components\n",
+    "# Custom components\n",
     "\n",
     "As I mentioned earlier in the example notebooks, and also in the `README`, it is possible to customise almost every component in `pytorch-widedeep`.\n",
     "\n",

mkdocs/sources/examples/09_extracting_embeddings.ipynb

 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extracting embeddings\n",
+    "\n",
+    "This notebook is a simple guide to extracting learned feature embeddings using Tab2Vec"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -494,7 +503,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.7 ('base')",
    "language": "python",
    "name": "python3"
   },
@@ -508,7 +517,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "3b99005fd577fa40f3cce433b2b92303885900e634b2b5344c07c59d06c8792d"
+   }
   }
  },
  "nbformat": 4,

mkdocs/sources/examples/11_auc_multiclass.ipynb

@@ -431,7 +431,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.9.7"
   },
   "toc": {
    "base_numbering": 1,

mkdocs/sources/examples/12_ZILNLoss_origkeras_vs_pytorch_multimodal.ipynb → mkdocs/sources/examples/12_ZILNLoss_origkeras_vs_pytorch_widedeep.ipynb

@@ -4,9 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# [DISCLAIMER]\n",
+    "# ZILNLoss\n",
     "\n",
-    "Purpose of this notebook is to check if ZILNloss [implemented originaly Keras](https://github.com/google/lifetime_value/blob/master/notebooks/kdd_cup_98/regression.ipynb) give same results in pytorch-multimodal implemenatation"
+    "**[DISCLAIMER]**\n",
+    "\n",
+    "Purpose of this notebook is to check if ZILNloss [implemented originaly Keras](https://github.com/google/lifetime_value/blob/master/notebooks/kdd_cup_98/regression.ipynb) give same results in pytorch-widedeep implemenatation"
    ]
   },
   {
@@ -59,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
     "id": "pBXE3Dz3NI4A"
    },
@@ -138,19 +140,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "id": "Dg3qtgJyJpdi"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Process is interrupted.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%bash\n",
     "mkdir -p /tmp/lifetime-value/kdd_cup_98\n",
@@ -164,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {
     "id": "a_LnLmQQRlYF"
    },
@@ -1218,7 +1212,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3.9.7 ('base')",
    "language": "python",
    "name": "python3"
   },
@@ -1232,7 +1226,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.7"
+   "version": "3.9.7"
   },
   "toc": {
    "base_numbering": 1,
@@ -1246,6 +1240,11 @@
    "toc_position": {},
    "toc_section_display": true,
    "toc_window_display": false
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "3b99005fd577fa40f3cce433b2b92303885900e634b2b5344c07c59d06c8792d"
+   }
   }
  },
  "nbformat": 4,

examples/notebooks/13_Model_Uncertainty_prediction.ipynb → mkdocs/sources/examples/13_model_uncertainty_prediction.ipynb

@@ -5,7 +5,11 @@
    "id": "57216283",
    "metadata": {},
    "source": [
-    "# Custom DataLoader for Imbalanced dataset"
+    "# Model Uncertainty prediction \n",
+    "\n",
+    "**Note**:\n",
+    "\n",
+    "This notebook extends the \"Custom DataLoader for Imbalanced dataset\" notebook"
    ]
   },
   {

mkdocs/sources/examples/14_bayesian_models.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## The Bayesian Models\n",
+    "# The Bayesian Models\n",
     "\n",
     "Perhaps one of the most interesting functionality in the library is the access to full Bayesian models in almost exactly the same way one would use any of the other models in the library. \n",
     "\n",