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.
Testing Loss: 1.516066481353371 Testing Accuracy: 0.4617999792098999
Testing Loss: 1.1094991602715414 Testing Accuracy: 0.6746999621391296
Testing Loss: 0.8757611672589733 Testing Accuracy: 0.7076999545097351
Testing Loss: 0.7395002558163017 Testing Accuracy: 0.8202999830245972
Testing Loss: 0.18219238568014304 Testing Accuracy: 0.9521999955177307