Digests » 18

this week's favorite

Generative Adversarial Networks – Paper Reading Road Map

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.

Collecting web data without an API

In this tutorial, we’ll show you how to extract data from Wikipedia pages.

Simple diagrams of convoluted neural networks

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?

How to learn Deep Learning in 6 months

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.

The Secret to Mastering ML

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.