Overview

The CIFAR-10 dataset has 10 categories: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, and truck.
Used Google Colab, a cloud-based platform, to train Convolutional Neural Network (CNN) models with and without transfer learning.
Transfer learning CNN model was trained on top of the VGG16 model of Keras.
This project was a 10-class classification problem.

Methodology

Polished the dataset by loading the data set from CIFAR-10 and splitting it into train and test data.
Upsampled the images from 32x32 pixels to 64x64 pixels to improve the performance of the model.
CNN model without transfer learning, applied four Convolutional layers with 32, 32, 64 and 64 neurons (kernels) respectively.
Applied kernel size of 3x3 and had two Max Pooling layers with pool sizes of 2x2 and 1x1 stride to reduce variances and computations for our model.
Had a Dense layer with 512 neurons and a Dropout layer with a probability of 50% to drop a neuron.