Digests » 49

this week's favorite

Neural networks and deep learning

One of the most striking facts about neural networks is that they can compute any function at all. That is, suppose someone hands you some complicated, wiggly function, f(x).

The AI Behind LinkedIn Recruiter Search and Recommendation Systems

In this blog post, we will first highlight a few unique information retrieval, system, and modeling challenges associated with talent search and recommendation systems. We will then describe how we formulated and addressed these challenges, the overall system design and architecture, the issues encountered in practice, and the lessons learned from the production deployment of these systems at LinkedIn.


An Interactive Introduction to Artificial Intelligence (AI).

BlingFire: A lightning fast Finite State machine and REgular expression manipulation library.

Hi, we are a team at Microsoft called Bling (Beyond Language Understanding), we help Bing be smarter. Here we wanted to share with all of you our FInite State machine and REgular expression manipulation library (FIRE). We use Fire for many linguistic operations inside Bing such as Tokenization, Multi-word expression matching, Unknown word-guessing, Stemming / Lemmatization just to mention a few.

A Brief Introduction To GANs (and how to code them)

GANs, or Generative Adversarial Networks, are a type of neural network architecture that allow neural networks to generate data. In the past few years, they’ve become one of the hottest subfields in deep learning, going from generating fuzzy images of digits to photorealistic images of faces.