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Facebook

Menlo Park, CA https://www.facebook.com
   
 
   
 

About Facebook Engineering

Facebook is one of the most-trafficked sites in the world and has had to build infrastructure to support this rapid growth. The company is the largest user in the world of memcached, an open source caching system, and has one of the largest MySQL database clusters anywhere.

Our development cycle is extremely fast, and we've built tools to keep it that way. It's common to write code and have it running on the live site a few days later. This comes as a pleasant surprise to engineers who have worked at other companies where code takes months or years to see the light of day. If you work for us, you will be able to make an immediate impact.

Small amjad masad Amjad Masad on

Advanced Techniques for Javascript Debugging

As the complexity of the apps we're building grows it becomes harder and harder to debug. Beyond your typical console.log and breakpoint insertion debugging Amjad Masad (Software Engineer, Facebook) discusses some of the lesser known JS debugging techniques that has helped him debug some of the more elusive bugs he’s seen while working on Facebook.

37:41

This talk was given at the NYCHTML5 meetup at Condé Nast.

See more great talks from their meetups here.

Small bennewman Ben Newman on

Regenerator, AST, and ES6 at Facebook

Ben Newman (Engineer, Facebook) has spent many months creating Regenerator. The open source project brings support for ES6 style generators to your code. In this popular talk, Ben will talk about what led him to create Regenerator, show us what it can do today, and give you a sneak peak of where it's going in the future.

01:08:38

This talk was presented at the nodejs meetup hosted by Pivotal Labs.

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Streaming Data Analysis and Online Learning by John Myles White

In this talk, "Streaming Data Analysis and Online Learning," John Myles White of Facebook surveys some basic methods for analyzing data in a streaming manner. He focuses on using stochastic gradient descent (SGD) to fit models to data sets that arrive in small chunks, discussing some basic implementation issues and demonstrating the effectiveness of SGD for problems like linear and logistic regression as well as matrix factorization. He also describes how these methods allow ML systems to adapt to user data in real-time. This talk was recorded at the New York Open Statistical Programming meetup at Knewton.

01:04:03

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