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.
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In this post, we will go through all the elements required to create and train AlexNet following the original paper. We will cover data processing, architecture definition, coding of training and validation loops, optimizations to speed up and training. Achieving comparable results with a top-1 error rate of 39.9% and top-5 error rate of 17.7%.