Merge pull request #231 from visual-layer/notebook-refactor

Notebook refactor

Author: amiralush

examples/analyzing-hf-datasets.ipynb

@@ -15,6 +15,9 @@
    "source": [
     "# Analyzing Hugging Face Datasets\n",
     "\n",
+    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/visual-layer/fastdup/blob/main/examples/analyzing-hf-datasets.ipynb)\n",
+    "[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/visual-layer/fastdup/blob/main/examples/analyzing-hf-datasets.ipynb)\n",
+    "\n",
     "This notebook shows how you can use fastdup to analyze any datasets from [Hugging Face Datasets](https://huggingface.co/docs/datasets/index).\n",
     "\n",
     "We will analyze an image classification dataset for:\n",
@@ -2579,18 +2582,30 @@
     "Try it out and let us know what issues you find.\n",
     "\n",
     "\n",
-    "We recommend checking out -\n",
+    "Next, feel free to check out other tutorials -\n",
     "\n",
-    "- [**Quick Dataset Analysis**](https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/quick-dataset-analysis.ipynb) - Learn how to quickly analyze a dataset for potential issues. Identify duplicates, outliers, dark/bright/blurry images, and cluster similar images with only a few lines of code.\n",
+    "+ ⚡ [**Quickstart**](https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/quick-dataset-analysis.ipynb): Learn how to install fastdup, load a dataset and analyze it for potential issues such as duplicates/near-duplicates, broken images, outliers, dark/bright/blurry images, and view visually similar image clusters. If you're new, start here!\n",
+    "+ 🧹 [**Clean Image Folder**](https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/cleaning-image-dataset.ipynb): Learn how to analyze and clean a folder of images from potential issues and export a list of problematic files for further action. If you have an unorganized folder of images, this is a good place to start.\n",
+    "+ 🖼 [**Analyze Image Classification Dataset**](https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/analyzing-image-classification-dataset.ipynb): Learn how to load a labeled image classification dataset and analyze for potential issues. If you have labeled ImageNet-style folder structure, have a go!\n",
+    "+ 🎁 [**Analyze Object Detection Dataset**](https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/analyzing-object-detection-dataset.ipynb): Learn how to load bounding box annotations for object detection and analyze for potential issues. If you have a COCO-style labeled object detection dataset, give this example a try. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08fd287b",
+   "metadata": {},
+   "source": [
     "\n",
-    "- [**Cleaning Image Dataset**](https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/cleaning-image-dataset.ipynb) -  Learn how to clean a dataset from broken images, duplicates, outliers, and identify dark/bright/blurry images.\n",
+    "# VL Profiler\n",
+    "If you prefer a no-code platform to inspect and visualize your dataset, [**try our free cloud product VL Profiler**](https://app.visual-layer.com) - VL Profiler is our first no-code commercial product that lets you visualize and inspect your dataset in your browser. \n",
     "\n",
-    "- [**Try our free cloud product VL Profiler**](https://app.visual-layer.com) - VL Profiler is our first no-code commercial product that lets you visualize and inspect your dataset in your browser.\n",
+    "[Sign up](https://app.visual-layer.com) now, it's free.\n",
     "\n",
     "[![image](https://raw.githubusercontent.com/visual-layer/fastdup/main/gallery/vl_profiler_promo.svg)](https://app.visual-layer.com)\n",
     "\n",
-    "As usual, feedback is welcome! Drop by our [Slack channel](https://visualdatabase.slack.com/join/shared_invite/zt-19jaydbjn-lNDEDkgvSI1QwbTXSY6dlA#/shared-invite/email) if you have questions!\n",
-    "Happy learning 😀"
+    "As usual, feedback is welcome! \n",
+    "\n",
+    "Questions? Drop by our [Slack channel](https://visualdatabase.slack.com/join/shared_invite/zt-19jaydbjn-lNDEDkgvSI1QwbTXSY6dlA#/shared-invite/email) or open an issue on [GitHub](https://github.com/visual-layer/fastdup/issues)."
    ]
   }
  ],