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CIFAR-10 (CNN) small photo classification problem is a standard dataset used in computer vision and deep learning for object recognition. These are very small images, much smaller than a

typical photograph, and the dataset was intended for computer vision research. Though it is easy to achieve 80% classification accuracy, it is possible to obtain top performance by

implementing Deep Learning Convolutional Neural Network with a classification accuracy above 90% on the test dataset. In this tutorial, you will discover how to unzip .tar.gz folder from

scratch and unpickle the dataset on Google Colab. STEP 1: Click on the link provided below. https://www.cs.toronto.edu/~kriz/cifar.html STEP 2: To download the dataset for Python click on

the highlighted part. It will take around 5–10 minutes to download the dataset. It will be saved as .tar.gz STEP 3: To extract this file we need to download an app 7-Zip from the link

provided below. STEP 4: Open the 7-Z App and provide the location where you have downloaded the dataset. STEP 5: Click on the dataset you have downloaded. STEP 6: Click on the highlighted

part. STEP 7: Click on COPY. STEP 8: Click on OK _You can check the saved folder in the location you saved it._ STEP 9: Upload this folder on google drive. STEP 10: Connect Google Colab with

Google Drive and unpickle the file. How to connect Google Colab with Google Drive: https://www.marktechpost.com/2019/06/07/how-to-connect-google-colab-with-google-drive/ ACKNOWLEDGEMENT

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