I come from the relational world like probably everyone who's been doing software for the last 5 years or so. In 2012 NoSQL databases were already a thing, and I had an opportunity to work with Google's Datastore, so I googled the documentation and started from the beginning.
At Google, researchers collaborate closely with product teams, applying the latest advances in Machine Learning to existing products and services - such as speech recognition in the Google app, search in Google Photos and the Smart Reply feature in Inbox by Gmail - in order to make them more useful. A growing number of Google products are using TensorFlow, our open source Machine Learning system, to tackle ML challenges and we would like to enable others do the same. Today, at GCP NEXT 2016, we announced the alpha release of Cloud Machine Learning, a framework for building and training custom models to be used in intelligent applications.
At the end of last year we released code that allows a user to classify images with TensorFlow models. This code demonstrated how to build an image classification system by employing a deep learning model that we had previously trained. This model was known to classify an image across 1000 categories supplied by the ImageNet academic competition with an error rate that approached human performance.
By Matthew Mayo, KDnuggets. KDnuggets has taken seriously its role to keep up with the newest releases of major deep learning projects, and in the recent past we have seen landmark such releases from major technology giants and as well as...
TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
On behalf of the community, it’s our pleasure to announce that Kubernetes, the open source container orchestration system, has reached the v1 milestone (GitHub). This important release, built by over 400 contributors, means Kubernetes is ready for production use. While this is huge news, there’s still much work remaining to build out the entire container toolset.
Google's Borg system is a cluster manager that runs hundreds of thousands of jobs, from many thousands of different applications, across a number of clusters each with up to tens of thousands of machines. It achieves high utilization by combining admission control, efficient task-packing, over-commitment, and machine sharing with process-level performance isolation. It supports high-availability applications with runtime features that minimize fault-recovery time, and scheduling policies that reduce the probability of correlated failures. Borg simplifies life for its users by offering a declarative job specification language, name service integration, real-time job monitoring, and tools to analyze and simulate system behavior.
We present a summary of the Borg system architecture and features, important design decisions, a quantitative analysis of some of its policy decisions, and a qualitative examination of lessons learned from a decade of operational experience with it.
Artificial Neural Networks have spurred remarkable recent progress in image classification and speech recognition. But even though these are very useful tools based on well-known mathematical methods, we actually understand surprisingly little of why certain models work and others don’t. So let’s take a look at some simple techniques for peeking inside these networks.
Last year we announced QUIC, a UDP-based transport protocol for the modern Internet. Over the last quarter, we’ve been increasing the amount of traffic to Google services that is served over QUIC and analyzing QUIC performance at scale. Results so far are positive, with the data showing that QUIC provides a real performance improvement over TCP thanks to QUIC's lower-latency connection establishment, improved congestion control, and better loss recovery.
At Google, we spend a lot of time thinking about how computer systems can read and understand human language in order to process it in intelligent ways. Today, we are excited to share the fruits of our research with the broader community by releasing SyntaxNet, an open-source neural network framework implemented in TensorFlow that provides a foundation for Natural Language Understanding (NLU) systems. Our release includes all the code needed to train new SyntaxNet models on your own data, as well as Parsey McParseface, an English parser that we have trained for you and that you can use to analyze English text.
Have you ever wondered how Google Photos helps you find all your favorite dog photos? With today’s release ofGoogle Cloud Vision API, developers can now build powerful applications that can see, and more importantly understand, the content of images. The uses of Cloud Vision API are game changing to developers of all types of applications and we are very excited to see what happens next!
Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.
Sharing your scoops to your social media accounts is a must to distribute your curated content. Not only will it drive traffic and leads through your content, but it will help show your expertise with your followers.
How to integrate my topics' content to my website?
Integrating your curated content to your website or blog will allow you to increase your website visitors’ engagement, boost SEO and acquire new visitors. By redirecting your social media traffic to your website, Scoop.it will also help you generate more qualified traffic and leads from your curation work.
Distributing your curated content through a newsletter is a great way to nurture and engage your email subscribers will developing your traffic and visibility.
Creating engaging newsletters with your curated content is really easy.