Update the jupyiter notebook links in example workflows section

examples/churn_prediction/Customer Churn Prediction.ipynb

@@ -1,13 +1,14 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "3637c5d6-6d94-4db5-ba31-5cea5f80b5b3",
    "metadata": {},
    "source": [
     "# Customer Churn Prediction\n",
     "\n",
-    "This is a tutorial that will walk you through creating your first useful workflow with Aqueduct. You can find and download this notebook on GitHub [here](https://github.com/aqueducthq/aqueduct/blob/main/examples/churn_prediction/Build%20and%20Deploy%20Churn%20Ensemble.ipynb).\n",
+    "This is a tutorial that will walk you through creating your first useful workflow with Aqueduct. You can find and download this notebook on GitHub [here](https://github.com/aqueducthq/aqueduct/blob/main/examples/churn_prediction/Customer%20Churn%20Prediction.ipynb).\n",
     "\n",
     "The philosophy behind the Aqueduct SDK is that you should be able to connect to your data systems, transform your data and generate predictions, and publish your results once you’re happy with them. This guide will walk you through installing your SDK, setting up a client, transforming some data, and publishing a workflow to the cloud. \n",
     "\n",

examples/house-price-prediction/House Price Prediction.ipynb

@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "a047703f",
    "metadata": {},
@@ -9,7 +10,7 @@
     "\n",
     "This notebook will take you through the process of setting up a workflow that featurizes a relatively complex [housing price dataset](https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques), creating 4 models th predict housing prices, and ensembling the results by taking the average of the 4 models. If you're curious, the original Kaggle competition has a full description of the dataset. \n",
     "\n",
-    "**You can find and download this notebook on GitHub [here](https://github.com/aqueducthq/aqueduct/blob/main/examples/house-price-prediction/House%20Price%20Prediciton.ipynb).**\n",
+    "**You can find and download this notebook on GitHub [here](https://github.com/aqueducthq/aqueduct/blob/main/examples/house-price-prediction/House%20Price%20Prediction.ipynb).**\n",
     "\n",
     "The credit for all the feature engineering that's done here goes to use Serigne on Kaggle, who put together this wonderful [notebook](https://www.kaggle.com/code/serigne/stacked-regressions-top-4-on-leaderboard/notebook) for this competition. \n",
     "\n",

examples/sentiment-analysis/Sentiment Model.ipynb

@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "213336fc-483b-4141-a07b-2e136014a5b7",
    "metadata": {},
@@ -9,7 +10,7 @@
     "\n",
     "This is a short example of how to use Aqueduct to deploy a sentiment analysis model.\n",
     "\n",
-    "**You can find and download this notebook on GitHub [here](https://github.com/aqueducthq/aqueduct/blob/main/examples/sentiment_analysis/Sentiment%20Model.ipynb).**\n",
+    "**You can find and download this notebook on GitHub [here](https://github.com/aqueducthq/aqueduct/blob/main/examples/sentiment-analysis/Sentiment%20Model.ipynb).**\n",
     "\n",
     "Note: This example workflow uses HuggingFace's [Transformers](https://huggingface.co/docs/transformers/index) package, which uses large models. If you're running on a resource constrained machine, or if you're running on an M1 Mac using Rosetta, you will likely run out of memory for these models. We recommend using another example workflow if this is the case.\n",
     "\n",

examples/tutorials/Parameters Tutorial.ipynb

@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "b4980764",
    "metadata": {},
@@ -10,7 +11,7 @@
     "\n",
     "This is a quick tutorial that will demonstrate how workflows can be parameterized with Aqeuduct.\n",
     "\n",
-    "**You can find and download this notebook on GitHub [here](https://github.com/aqueducthq/aqueduct/blob/main/examples/parameterization/Using%20Parameters.ipynb).**\n",
+    "**You can find and download this notebook on GitHub [here](https://github.com/aqueducthq/aqueduct/blob/main/examples/tutorials/Parameters%20Tutorial.ipynb).**\n",
     "\n",
     "**Throughout this notebook, you'll see a decorator (`@aq.op`) above functions. This decorator allows Aqueduct to run your functions as a part of a workflow automatically.**"
    ]
@@ -1237,4 +1238,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}