WHY IT MATTERS: Digital Transformation

At LinkedIn, we use machine learning technology widely to optimize our products: for instance, ranking search results, advertisements, and updates in the news feed, or recommending people, jobs, articles, and learning opportunities to members. An important component of this technology stack is a knowledge graph that provides input signals to machine learning models and data insight pipelines to power LinkedIn products. This post gives an overview of how we build this knowledge graph.

No one really knows how the most advanced algorithms do what they do. That could be a problem.

A curated list of 700+ engineering blogs.

Is the future of content management decoupled? Centralization around a single source of content has never been more important as marketers aim to reach

Learn more about the implications of the JAMstack (JavaScript, APIs & Markup) & static site CMS for non-technical clients & businesses.

A SaaS company like Dropbox needs to update our systems constantly, at all levels of the stack. When it comes time to tune some piece of infrastructure, roll out a new feature, or set up an A/B test, it’s important that we can make changes and have them hit production fast.
Making a change to our code and then “simply” pushing it is not an option: completing a push to our web servers can take hours, and shipping a new mobile or desktop platform release takes even longer. In any case, a full code deployment can be dangerous because it could introduce new bugs: what we really want is a way to put some configurable “knobs” into our products, which a) give us the flexibility we need and b) can be safely tweaked in near real-time.
To satisfy this need, we built a system called Stormcrow, which allows us to edit and deploy “feature gates.” A feature gate is a configurable code path that calls out to Stormcrow to determine how to proceed.

Maybe you’ve downloaded TensorFlow and you’re ready to get started with some deep learning?

But then you wonder: What the hell is a tensor?

Perhaps you looked it up on Wikipedia and now you’re more confused than ever. Maybe you found this NASA tutorial and still have no idea what it’s talking about?

The problem is most guides talk about tensors as if you already understand all the terms they’re using to describe the math. Have no fear! I hated math as a kid, so if I can figure it out, you can too! We just have to explain everything in simpler terms.

Welcome to the first in a series of blog posts that is designed to get you quickly up to speed with deep learning; from first principles, all the way to discussions of some of the intricate details, with the purposes of achieving respectable performance on two established machine learning benchmarks: MNIST (classification of handwritten digits) and CIFAR-10 (classification of small images across 10 distinct classes—airplane, automobile, bird, cat, deer, dog, frog, horse, ship & truck).

For the past five years, our Android project has maintained a similar package structure from when it was first created in 2012. 

We also want to keep our package structure clean. This rethought is about keeping the workspace that we interact with on a daily basis both tidy and organized.

So we set about to rethink the entire package structure of the Buffer Android app. I’m excited to share what our process looked like and all that we learned, and it’d be great to hear your thoughts and questions, too.