/Machine Learning

How To Trick A Neural Network In Python 3

- Alvin Wan tl;dr: A tutorial that teaches you to trick an animal classifier and an understanding of how to defend against such tricks.

featured in #190

Face Detection Using JavaScript API - face-api.js

- Deepak Gupta tl;dr: Built on TensorFlow, this solves face detection, face recognition and face landmark detection, optimized for web and mobile devices.

featured in #157

Code As Craft: Understand The Role Of Style In E-commerce Shopping

- Aakash Sabharwal Jingyuan Zhou tl;dr: Overview of how Etsy built a ML tool that analyzes a user's taste and subsequently generates personalized recommendations.

featured in #150

Finding The Point Of Human Leverage

- Ben Evans tl;dr: Large platforms are mechanical turks relying on users to "create, capture and channel human annotation" e.g. FB knows your likes once you hit "like", in doing so it has found a point of leverage. Such platforms need a lot of users but, crucially, ML doesn't. This will change the point of leverage.

featured in #137

Bringing Black And White Photos To Life Using Colourise.sg 

- Preston Lim tl;dr: Using a deep learning technique called GANs, Colourise.sg predicts the color values at each pixel in black & white images. The model is trained on old Singaporean photos. In order to create accuracy, colorization requires historical, geographic, and cultural context. Below is an example, you can try the tool here on your own B&W photos.

featured in #132