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Digests » 18
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This summer, I have worked on Generative Adversarial Networks (GANs) through my research internship. At first, I did not know much about this model, so the very first weeks of my internship included a lot of paper reading. To help the others who want to learn more about the technical sides of GANs, I wanted to share some papers I have read in the order that I read them.
In this tutorial, we’ll show you how to extract data from Wikipedia pages.
Neural networks are complicated, multidimensional, nonlinear array operations. How can we present a deep learning model architecture in a way that shows key features, while avoiding being too complex or repetitive? How can we present them in a way that is clear, didactic and insightful?
It is quite possible to learn, follow and contribute to state-of-art work in deep learning in about 6 months’ time. This article details out the steps to achieve that.
A lot of bullshit guides and articles tell you do this course or that certificate, and that’s fine for foundational learning, but the true road to understanding at an elite level is building something cool and new. Building things and sharing them with the public forces you to introduce a structure, rigor, and integrity to your work, and allows you to truly grasp the concepts that are at play.