Implementing a ResNet model from scratch. – Towards Data Science

When implementing the ResNet architecture in a deep learning project I was working on, it was a huge leap from the basic, simple convolutional neural networks I was used to. One prominent feature of ResNet is that it utilizes a micro-architecture within it's larger macroarchitecture: residual blocks! I decided to look into the model myself to gain a better understanding of it, as well as look into why it was so successful at ILSVRC. I implemented the exact same ResNet model class in Deep Learning for Computer Vision with Python by Dr. Adrian Rosebrock [1], which followed the ResNet model from the 2015 ResNet academic publication, Deep Residual Learning for Image Recognition by He et al. [2]. When ResNet was first introduced, it was revolutionary for proving a new solution to a huge problem for deep neural networks at the time: the vanishing gradient problem.