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.
Interactive calculator to compute the output shape of a convolutional layer based on the input, kernel and other settings. It supports non-square kernels, stride and padding.
This post will show up on its own! In this post, I take a step back and reflect on the learning journey with Astro so far. I share some insights on the future direction I plan to take with this blog, potential improvements I’m excited about, and some new ideas I’ll be exploring next. Stay tuned for more content on advanced Astro topics coming soon.
An introduction to the basic concepts and building blocks of Convolution Neural Networks CNNs covering theory, best practices and practical widgets.
Let's understand Hopfield networks, introduced by John Hopefield in 'Neural networks and physical systems with emergent collective computational abilities' in 1982.
This is my first-ever post! I will explain my motivation for creating this space, the content I'd like to write about and the peculiarities that I'd like to create here.