Supervised machine learning is a widely used form of artificial intelligence. There are plenty of ways to approach supervised learning: some of them being Neural Networks, Convolutional Neural Networks and Residual Networks. In this repository we develop an in depth analysis of the difference between these on the CIFAR10 classification task.
CIFAR10 dataset. Sample of images with corresponding labels.
Testing Loss: 1.516 Testing Accuracy: 0.462
Testing Loss: 1.109 Testing Accuracy: 0.675
Testing Loss: 0.876 Testing Accuracy: 0.708
Testing Loss: 0.740 Testing Accuracy: 0.820
Testing Loss: 0.182 Testing Accuracy: 0.952