Add badges to the vector visualization notebook

Signed-off-by: ChengZi <[email protected]>

bootcamp/tutorials/quickstart/vector_visualization.ipynb

@@ -2,15 +2,23 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/milvus-io/bootcamp/blob/master/bootcamp/tutorials/quickstart/vector_visualization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>   <a href=\"https://github.com/milvus-io/bootcamp/blob/master/bootcamp/tutorials/quickstart/vector_visualization.ipynb\" target=\"_blank\">\n",
+    "    <img src=\"https://img.shields.io/badge/View%20on%20GitHub-555555?style=flat&logo=github&logoColor=white\" alt=\"GitHub Repository\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "# Vector Visualization\n",
     "In this example, we will show how to visualize the embeddings(vectors) in Milvus using [t-SNE](https://www.wikiwand.com/en/articles/T-distributed_stochastic_neighbor_embedding).\n",
     "\n",
     "Dimensionality reduction techniques, such as t-SNE, are invaluable for visualizing complex, high-dimensional data in a 2D or 3D space while preserving the local structure. This enables pattern recognition, enhances understanding of feature relationships, and facilitates the interpretation of machine learning model outcomes. Additionally, it aids in algorithm evaluation by visually comparing clustering results, simplifies data presentation to non-specialist audiences, and can reduce computational costs by working with lower-dimensional representations. Through these applications, t-SNE not only helps in gaining deeper insights into datasets but also supports more informed decision-making processes."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
@@ -618,17 +626,17 @@
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "As we can see, the query vector is close to the retrieved vectors. Although the retrieved vectors are not within a standard circle with a fixed radius centered on the query, we can see that they are still very close to the query vector on the 2D plane.\n",
-    "\n",
-    "Using dimensionality reduction techniques can facilitate the understanding of vectors and troubleshooting. Hope you can get a better understanding of vectors through this tutorial."
-   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%% md\n"
     }
-   }
+   },
+   "source": [
+    "As we can see, the query vector is close to the retrieved vectors. Although the retrieved vectors are not within a standard circle with a fixed radius centered on the query, we can see that they are still very close to the query vector on the 2D plane.\n",
+    "\n",
+    "Using dimensionality reduction techniques can facilitate the understanding of vectors and troubleshooting. Hope you can get a better understanding of vectors through this tutorial."
+   ]
   }
  ],
  "metadata": {
@@ -652,4 +660,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}