WHY IT MATTERS: Digital Transformation

Finally, though, the biggest surprise with this project was just how long it took - and related, the amount of compute resources it needed. The first surprise was in terms of calendar time. My original estimate was that as a side project it would take about 3 months. It actually took around 8 months. (And the original estimate was supposed to be pessimistic!) Some of that was down to underestimating how many hours each stage would take, but a big chunk of the underestimate was failing to anticipate other things coming up outside the project. It’s hard to say how well this generalises, but for side projects, taking your original (already pessimistic) time estimates and doubling them might not be a bad rule-of-thumb.

Apache PredictionIO is an open source Machine Learning Server built on top of a state-of-the-art open source stack for developers and data scientists to create predictive engines for any machine learning task.

This guide is for anyone who is curious about machine learning but has no idea where to start. I imagine there are a lot of people who tried reading the wikipedia article, got frustrated and gave up wishing someone would just give them a high-level explanation. That’s what this is. The goal is be accessible to anyone — which means that there’s a lot of generalizations. But who cares? If this gets anyone more interested in ML, then mission accomplished.

This technology is called face recognition. Facebook’s algorithms are able to recognize your friends’ faces after they have been tagged only a few times. It’s pretty amazing technology — Facebook can recognize faces with 98% accuracy which is pretty much as good as humans can do!

Creating and maintaining a platform for reliably producing and deploying machine learning models requires careful orchestration of many components—-a learner for generating models based on training data, modules for analyzing and validating both data as well as models, and finally infrastructure for serving models in production. This becomes particularly challenging when data changes over time and fresh models need to be produced continuously. Unfortunately, such orchestration is often done ad hoc using glue code and custom scripts developed by individual teams for specific use cases, leading to duplicated effort and fragile systems with high technical debt. We present the anatomy of a general-purpose machine learning platform and one implementation of such a platform at Google. By integrating the aforementioned components into one platform, we were able to standardize the components, simplify the platform configuration, and reduce the time to production from the order of months to weeks, while providing platform stability that minimizes service disruptions. We present the case study of one deployment of the platform in the Google Play app store, where the machine learning models are refreshed continuously as new data arrive. Deploying the platform led to reduced custom code, faster experiment cycles, and a 2% increase in app installs resulting from improved data and model analysis.

Developers have built more than 15,000 skills with the Alexa Skills Kit. Explore the stories behind some of these innovations, then start building your own skill. If you’re serious about getting into building voice UIs, we’d like to help you explore. 

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).