Refactored

Author: bansalkanav

Module 4 - Machine Learning/03. Working with Image Data/1. Intro to Image Processing/basic_image_processing.ipynb

@@ -737,7 +737,7 @@
  ],
  "metadata": {
  "kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
  "language": "python",
  "name": "python3"
  },
@@ -751,7 +751,7 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.8.5"
+ "version": "3.9.13"
  },
  "toc": {
  "nav_menu": {},
@@ -796,5 +796,5 @@
  }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

Module 4 - Machine Learning/03. Working with Image Data/1. Intro to Image Processing/images/transformed/black_with_white_dot.jpg renamed to Module 4 - Machine Learning/03. Working with Image Data/1. Intro to Image Processing/images/transformed/black_with_rgb_dot.jpg

@@ -0,0 +1,7 @@
+About Dataset
+
+Dataset contains about 28K medium quality animal images belonging to 10 categories: dog, cat, horse, spyder, butterfly, chicken, sheep, cow, squirrel, elephant.
+
+All the images have been collected from "google images" and have been checked by human. There is some erroneous data to simulate real conditions (eg. images taken by users of your app).
+
+The main directory is divided into folders, one for each category. Image count for each category varies from 2K to 5 K units.

Module 4 - Machine Learning/03. Working with Image Data/3. Image Vectorization/image_vectorization.ipynb

@@ -0,0 +1 @@
+translate = {"cane": "dog", "cavallo": "horse", "elefante": "elephant", "farfalla": "butterfly", "gallina": "chicken", "gatto": "cat", "mucca": "cow", "pecora": "sheep", "scoiattolo": "squirrel", "dog": "cane", "cavallo": "horse", "elephant" : "elefante", "butterfly": "farfalla", "chicken": "gallina", "cat": "gatto", "cow": "mucca", "spider": "ragno", "squirrel": "scoiattolo"}