Let's understand Hopfield networks, introduced by John Hopefield in 'Neural networks and physical systems with emergent collective computational abilities' in 1982.
Let's dive into the paper 'ImageNet Classification with Deep Convolutional Neural Networks' by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, which introduced the AlexNet to the world, and became a pivotal moment in the fields of computer vision and deep learning. The goal here is to explore in-depth the achievements, architecture, and details as a previous step toward its implementation.
Continuing our exploration of foundational deep learning models in computer vision, we will dive into the 2014 paper Very Deep Convolutional Networks for Large-Scale Image Recognition by Karen Simonyan and Andrew Zisserman, which introduced VGGNet, a set of simple yet highly performant networks. We will examine its architecture, data processing, training, testing, and analysis of the results as a preliminary step toward implementing it.