clean and doc

clean and doc

Author: duchaba2

Chapter_1/data_augmentation_with_python_chapter_1.ipynb

@@ -4,7 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyMQ+1o267nUQYSR0kPcdzHU",
+      "authorship_tag": "ABX9TyMC2dGPK2A6zyHMp+8cnqEg",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -38,7 +38,9 @@
         "\n",
         "I am glad to see you using this Python Notebook. 🐕\n",
         "\n",
-        "The Python Notebook is an integral part of the book. You can add new “code cells” to extend the functions, add your data, and explore new possibilities, such as downloading additional real-world datasets from the Kaggle website and coding the **Fun challenges**. The **Fun facts**, an in-depth discussion of the augmentation methods, and Pluto, an imaginary Siberian Huskey coding companion, will guide you step by step. Pluto encourages adding “text cells” to keep your note in the Python Notebook. In other words, read the book and copy the relevant concept to this Python Notebook’s text-cells. Thus, you can have the explanation, note, original code, your code, and any crazy future ideas in one place.  \n",
+        "The Python Notebook is an integral part of the book. You can add new “code cells” to extend the functions, add your data, and explore new possibilities, such as downloading additional real-world datasets from the Kaggle website and coding the **Fun challenges**. Furthermore, the book has **Fun facts**, in-depth discussion about augmentation techniques, and Pluto, an imaginary Siberian Huskey coding companion. Together they will guide you every steps of the way. \n",
+        "\n",
+        "Pluto encourages you to copy or save a copy of this Python Notebook to your local space and add the “text cells” to keep your notes. In other words, read the book and copy the relevant concept to this Python Notebook’s text-cells. Thus, you can have the explanation, note, original code, your code, and any crazy future ideas in one place.  \n",
         "\n",
         "\n",
         "💗 I hope you enjoy reading the book and hacking code as much as I enjoy writing it. \n",
@@ -68,7 +70,7 @@
     {
       "cell_type": "markdown",
       "source": [
-        "## 😀 Excerpt from Chapter 1, Data Augmentation Made Easy\n",
+        "# 😀 Excerpt from Chapter 1, Data Augmentation Made Easy\n",
         "\n",
         "---\n",
         "\n",
@@ -131,10 +133,22 @@
         "!git --version"
       ],
       "metadata": {
-        "id": "AEoYuTg6y1YN"
+        "id": "AEoYuTg6y1YN",
+        "outputId": "cccbac02-c13f-45d9-878a-89bb8121d7e9",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
       },
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "git version 2.25.1\n"
+          ]
+        }
+      ]
     },
     {
       "cell_type": "code",
@@ -143,10 +157,28 @@
         "!git clone {url}"
       ],
       "metadata": {
-        "id": "BgGgEP3SytI4"
+        "id": "BgGgEP3SytI4",
+        "outputId": "76f8653b-22b2-431e-bb30-2cf284bf6ce8",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
       },
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Cloning into 'Data-Augmentation-with-Python'...\n",
+            "remote: Enumerating objects: 126, done.\u001b[K\n",
+            "remote: Counting objects: 100% (104/104), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (85/85), done.\u001b[K\n",
+            "remote: Total 126 (delta 52), reused 54 (delta 18), pack-reused 22\u001b[K\n",
+            "Receiving objects: 100% (126/126), 4.28 MiB | 6.78 MiB/s, done.\n",
+            "Resolving deltas: 100% (52/52), done.\n"
+          ]
+        }
+      ]
     },
     {
       "cell_type": "code",
@@ -242,10 +274,26 @@
         "pluto = PacktDataAug(\"Pluto\")"
       ],
       "metadata": {
-        "id": "rtOsxJVNC8AV"
+        "id": "rtOsxJVNC8AV",
+        "outputId": "7a4fdf04-6d36-44bd-ce42-3d55246b00ba",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
       },
       "execution_count": null,
-      "outputs": []
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "---------------------------- : ----------------------------\n",
+            "            Hello from class : <class '__main__.PacktDataAug'> Class: PacktDataAug\n",
+            "                   Code name : Pluto\n",
+            "                   Author is : Duc Haba\n",
+            "---------------------------- : ----------------------------\n"
+          ]
+        }
+      ]
     },
     {
       "cell_type": "code",

Chapter_2/data_augmentation_with_python_chapter_2.ipynb

@@ -18,15 +18,14 @@
       "source": [
         "# 🌻 Welcome to Chapter 2, Biases In Data Augmentation\n",
         "\n",
-        "\n",
-        "\n",
         "---\n",
         "\n",
         "I am glad to see you using this Python Notebook. 🐕\n",
         "\n",
-        "The Python Notebook is an integral part of the book. You can add new “code cells” to extend the functions, add your data, and explore new possibilities, such as downloading additional real-world datasets from the Kaggle website and coding the **Fun challenges**. The **Fun facts**, an in-depth discussion of the augmentation methods, and Pluto, an imaginary Siberian Huskey coding companion, will guide you step by step. \n",
+        "The Python Notebook is an integral part of the book. You can add new “code cells” to extend the functions, add your data, and explore new possibilities, such as downloading additional real-world datasets from the Kaggle website and coding the **Fun challenges**. Furthermore, the book has **Fun facts**, in-depth discussion about augmentation techniques, and Pluto, an imaginary Siberian Huskey coding companion. Together they will guide you every steps of the way. \n",
+        "\n",
+        "Pluto encourages you to copy or save a copy of this Python Notebook to your local space and add the “text cells” to keep your notes. In other words, read the book and copy the relevant concept to this Python Notebook’s text-cells. Thus, you can have the explanation, note, original code, your code, and any crazy future ideas in one place.  \n",
         "\n",
-        "Pluto encourages adding “text cells” to keep your note in the Python Notebook. In other words, read the book and copy the relevant concept to this Python Notebook’s text-cells. Thus, you can have the explanation, note, original code, your code, and any crazy future ideas in one place.  \n",
         "\n",
         "💗 I hope you enjoy reading the book and hacking code as much as I enjoy writing it. \n",
         "\n",
@@ -44,6 +43,7 @@
         "\n",
         "\n",
         "- The original Python Notebook is on: \n",
+        "\n",
         "  - https://github.com/PacktPublishing/Data-Augmentation-with-Python/blob/main/Chapter_2/data_augmentation_with_python_chapter_2.ipynb \n",
         "\n",
         "- 🚀 Click on the blue \"Open in Colab\" button at the top of this page to begin hacking.\n"
@@ -1609,7 +1609,7 @@
     "colab": {
       "provenance": [],
       "toc_visible": true,
-      "authorship_tag": "ABX9TyOfZ1nzgrtjaM880qG1Jq2d",
+      "authorship_tag": "ABX9TyMKvpxiMyAgoI2S17vYEgTx",
       "include_colab_link": true
     },
     "gpuClass": "standard",