From 23fb75df3d55ee28a65fafe95bd04b9155b86b39 Mon Sep 17 00:00:00 2001
From: ejolly <eshin.jolly@gmail.com>
Date: Wed, 11 Aug 2021 16:23:00 -0400
Subject: [PATCH] minor readme and docs site fixes

---
 README.md                       |  2 +-
 docs/examples/dense-data.ipynb  | 12 +++++++-----
 docs/examples/sparse-data.ipynb |  2 +-
 docs/index.md                   |  5 +++--
 4 files changed, 12 insertions(+), 9 deletions(-)

diff --git a/README.md b/README.md
index de1591e..be34262 100644
--- a/README.md
+++ b/README.md
@@ -13,7 +13,7 @@
 
 ## Getting started
 
-The best way to learn how to use the package is by checking out the 3 usage tutorials on working with [dense](examples/dense-data), [sparse](examples/sparse-data), and [time-series](examples/timeseries-data). For more detailed usage on specific function arguments and model parameters check out the API reference on the left. 
+The best way to learn how to use the package is by checking out the [ documentation site](https://cosanlab.github.io/neighbors) which contains usage tutorials as well as API documentation for all package functionality.
 
 ### Quick Demo Usage
 
diff --git a/docs/examples/dense-data.ipynb b/docs/examples/dense-data.ipynb
index d4d2de0..b2759d3 100644
--- a/docs/examples/dense-data.ipynb
+++ b/docs/examples/dense-data.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "source": [
     "import seaborn as sns\n",
     "import matplotlib.ticker as ticker\n",
@@ -1152,16 +1152,15 @@
    "source": [
     "# Summary\n",
     "\n",
-    "Those are the basics of working with models and dense data. The [next tutorial](/examples/sparse-data) illustrates the primary use case for the toolbox: working with sparse data."
+    "Those are the basics of working with models and dense data. The [next tutorial](https:/cosanlab.github.io/neighbors/examples/sparse-data) illustrates the primary use case for the toolbox: working with sparse data."
    ],
    "metadata": {}
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:collab_filter]",
-   "language": "python",
-   "name": "conda-env-collab_filter-py"
+   "name": "python3",
+   "display_name": "Python 3.8.2 64-bit ('collab_filter': conda)"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1187,6 +1186,9 @@
    "toc_position": {},
    "toc_section_display": true,
    "toc_window_display": false
+  },
+  "interpreter": {
+   "hash": "a1513f7269d9ecc57456e5ae4df3eafd560085bab6dfa98ba020aafdc6c1a6b5"
   }
  },
  "nbformat": 4,
diff --git a/docs/examples/sparse-data.ipynb b/docs/examples/sparse-data.ipynb
index 4334051..baf9999 100644
--- a/docs/examples/sparse-data.ipynb
+++ b/docs/examples/sparse-data.ipynb
@@ -1039,7 +1039,7 @@
     "\n",
     "Fitting a model to sparse data is very similar to working with dense data with the exception that missing ratings have no ground truth for model performance calculation. However, the `estimate_performance` function can be used to approximate performance via cross-validation. This approach is the defacto standard in several other collaborative filtering toolboxes such as [Surprise](http://surpriselib.com/). \n",
     "\n",
-    "In the [last tutorial](/examples/timeseries-data) we'll see one more feature that's particular useful for working with user ratings that were collected _over time_. "
+    "In the [last tutorial](https://cosanlab.github.io/neighbors/examples/timeseries-data) we'll see one more feature that's particular useful for working with user ratings that were collected _over time_. "
    ],
    "metadata": {}
   }
diff --git a/docs/index.md b/docs/index.md
index bc05ea1..089b79e 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -2,7 +2,8 @@
 [![Build Status](https://github.com/cosanlab/neighbors/actions/workflows/tests_and_docs.yml/badge.svg)](https://github.com/cosanlab/neighbors/actions/workflows/tests_and_docs.yml)
 [![Coverage Status](https://coveralls.io/repos/github/cosanlab/neighbors/badge.svg?branch=master)](https://coveralls.io/github/cosanlab/neighbors?branch=master)
 ![Python Versions](https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-blue)
-![Platforms](https://img.shields.io/badge/platform-linux%20%7C%20osx%20%7C%20win-blue)
+![Platforms](https://img.shields.io/badge/platform-linux%20%7C%20osx%20%7C%20win-blue)  
+
 **A Python package for collaborative filtering on social and emotion datasets**
 
 ## Installation
@@ -12,7 +13,7 @@
 
 ## Getting started
 
-The best way to learn how to use the package is by checking out the 3 usage tutorials on working with [dense](examples/dense-data), [sparse](examples/sparse-data), and [time-series](examples/timeseries-data). For more detailed usage on specific function arguments and model parameters check out the API reference on the left. 
+The best way to learn how to use the package is by checking out the 3 usage tutorials on working with [dense](https://cosanlab.github.io/neighbors/examples/dense-data), [sparse](https://cosanlab.github.io/neighbors/examples/sparse-data), and [time-series](https://cosanlab.github.io/neighbors/examples/timeseries-data). For more detailed usage on specific function arguments and model parameters check out the API reference on the left. 
 
 ### Quick Demo Usage