update the introduction

update the introduction

Author: duchaba2

Chapter_1/data_augmentation_with_python_chapter_1.ipynb

@@ -4,7 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyNy8FkAGn4E4n9p2HthAnNz",
+      "authorship_tag": "ABX9TyO20h+Op0dHnh+NC4tIIUcB",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -28,24 +28,34 @@
         "<a href=\"https://colab.research.google.com/github/duchaba/Data-Augmentation-with-Python/blob/main/data_augmentation_with_python_chapter_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "# 💚 Data Augmentation with Python, Chapter 1"
-      ],
-      "metadata": {
-        "id": "qtnHR_uG0m7Z"
-      }
-    },
     {
       "cell_type": "markdown",
       "source": [
         "# 🌻 Welcome to Chapter 1, Data Augmentation Made Easy\n",
         "\n",
-        "#### I am glad to see you using this Python Notebook. It is an integral part of the book. You are free to 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**. I also encourage you to add “text cells” to keep your note in the Python notebook. \n",
         "\n",
-        "####The book contains in-depth augmentation concepts and code explanations. I hope you enjoy reading the book and hacking code on this Python Notebook as much as I enjoy writing it. \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**, in-depth discussion of the augmentation methods and Pluto, and 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 information, the code, and idea in one place. \n",
+        "\n",
+        "\n",
+        "💗 I hope you enjoy reading the book and hacking code as much as I enjoy writing it. \n",
+        "\n",
+        "\n",
+        "## 🌟 Amazon Book\n",
+        "\n",
+        "---\n",
+        "\n",
+        "- The book is available on the Amazon Book website: \n",
+        "  - https://www.amazon.com/dp/1803246456\n",
+        "\n",
+        "\n",
+        "- The original Python Notebook is on: \n",
+        "  - https://github.com/PacktPublishing/Data-Augmentation-with-Python/blob/main/Chapter_1/data_augmentation_with_python_chapter_1.ipynb \n",
+        "\n",
+        "- 🚀 Click on the blue \"Open in Colab\" button at the top of this page to begin hacking.\n"
       ],
       "metadata": {
         "id": "a5EqgaYW08Cm"
@@ -54,21 +64,24 @@
     {
       "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",
-        "> In case you haven’t bought the book. Here is an excerpt from the first page of Chapter 1. The book is on the Amazon Book website: https://www.amazon.com/dp/1803246456\n",
+        "> In case you haven’t bought the book. Here is an teaser from the first page of Chapter 1.\n",
         "\n",
+        "---\n",
         "\n",
         "Data augmentation is essential for developing a successful Deep Learning (DL) project. However, data scientists and developers often overlook this crucial step. It is not a secret that you will spend the majority of the project time gathering, cleaning, and augmenting the dataset in a real-world DL project. Thus, learning how to expand the dataset without purchasing new data is essential.This book covers standard and advanced techniques for extending the image, text, audio, and tabular dataset. Furthermore, there is a discussion on data biases, and the coding lessons are on Jupyter Python Notebooks.  \n",
         "\n",
         "Chapter 1 introduces the data augmentation concepts, sets up the coding environment, creates the foundation class, and later chapters explain techniques in detail, including Python coding. The effective use of data augmentation is the proven technique between success and failure in Machine Learning (ML). Many real-world ML projects stay in the conceptual phase because of insufficient data for training the ML model. Data augmentation is a cost-effective technique to increase the dataset, lower the training error rate, and produce a more accurate prediction and forecast. \n",
         "\n",
-        "\n",
+        "---\n",
         ">**Fun fact**\n",
         "\n",
         ">The car gasoline analogy is helpful for students who first learn about data augmentation and AI. You can think of data for the AI engine as the gasoline and data augmentation as the additive, like the Chevron Techron fuel cleaner, that makes your car engine run faster, smoother, and further without extra petrol.\n",
         "\n",
-        "\n",
+        "---\n",
         "\n",
         "In Chapter 1, we’ll define the data augmentation role and the limitation of how much to extend the data without changing the data integrity. We’ll briefly discuss the different types of input data, such as image, text, audio, and tabular data, and the challenges in supplementing the data. Finally, we’ll set up the system requirements and the programming style in the accompanying Jupyter Python Notebook.   \n",
         "\n",
@@ -90,7 +103,9 @@
         "\n",
         "Let’s start with augmentation role. \n",
         "\n",
-        "[end of excerpt]"
+        "---\n",
+        "\n",
+        "🌴 *end of excerpt from the book*\n"
       ],
       "metadata": {
         "id": "0Mo-idJS3eDh"