The Gluon interface currently works with Apache MXNet and will support Microsoft Cognitive Toolkit (CNTK) in an upcoming release. With the Gluon interface, developers can build machine learning models using a simple Python API and a range of pre-built, optimized neural network components. This makes it easier for developers of all skill levels to build neural networks using simple, concise code, without sacrificing performance. AWS and Microsoft published Gluon's reference specification so other deep learning engines can be integrated with the interface.

Driven by customer demand and made possible by on-going advances in the state-of-the-art, we've come a long way since the original m1.small instance that we launched in 2006, with instances that emphasize compute power, burstable performance, memory size, local storage, and accelerated computing. The New P3 Today we are making the next generation of GPU-powered EC2 instances available in four AWS regions. Powered by up to eight NVIDIA Tesla V100 GPUs, the P3 instances are designed to handle compute-intensive machine learning, deep learning, computational fluid dynamics, computational finance, seismic analysis, molecular modeling, and genomics workloads. P3 instances use customized Intel Xeon E5-2686v4 processors running at up to 2.7 GHz. Each of the NVIDIA GPUs is packed with 5,120 CUDA cores and another 640 Tensor cores and can deliver up to 125 TFLOPS of mixed-precision floating point, 15.7 TFLOPS of single-precision floating point, and 7.8 TFLOPS of double-precision floating point.

Customer churn is a problem that all companies need to monitor, especially those that depend on subscription-based revenue streams. The simple fact is that most organizations have data that can be used to target these individuals and to understand the key drivers of churn, and we now have Keras for Deep Learning available in R (Yes, in R!!), which predicted customer churn with 82% accuracy. We're super excited for this article because we are using the new keras package to produce an Artificial Neural Network (ANN) model on the IBM Watson Telco Customer Churn Data Set! As for most business problems, it's equally important to explain what features drive the model, which is why we'll use the lime package for explainability. In addition, we use three new packages to assist with Machine Learning (ML): recipes for preprocessing, rsample for sampling data and yardstick for model metrics. These are relatively new additions to CRAN developed by Max Kuhn at RStudio (creator of the caret package). It seems that R is quickly developing ML tools that rival Python. Good news if you're interested in applying Deep Learning in R! We are so let's get going!! Customer churn refers to the situation when a customer ends their relationship with a company, and it's a costly problem. Customers are the fuel that powers a business. Further, it's much more difficult and costly to gain new customers than it is to retain existing customers. As a result, organizations need to focus on reducing customer churn. The good news is that machine learning can help. For many businesses that offer subscription based services, it's critical to both predict customer churn and explain what features relate to customer churn.

To fans of science fiction, artificial intelligence may remind them of robots like C-3PO, the loquacious but harmless golden droid in Star Wars, or Skynet in the Terminator movies, a calculating sentient computer that subjugated mankind. But AI is more than just a machine with human-level intelligence scientists hope they could one day create. It is a set of algorithms and technologies that is already powering many tasks in everyday life. Chatbots that converse with you in Yahoo, Facebook and other sites use AI. Alibaba harnesses deep learning to find a handbag matching the one in the photo you uploaded to its shopping site.

Neural networks aren't the right solution for everything, but they excel at dealing with complex data. Most of the time applying AI in mobile is still associated with incorporation of personal assistants, however, there is a larger perspective to that – AI is used to study our habits inside of various apps and better serve us. Thanks to AI solutions, app creators and marketers are now closer to understanding user behavior on the app through actions, likes, preferences, purchases and more. Deep Learning is the science to'teach' the machines recognize patterns and then apply these'learnings' to solve various complex queries. It also allows to learn user's behavior patterns in order to make the future sessions personalized and seamless.

Over the last few years, the quest to build fully autonomous vehicles has shifted into high gear. Yet, despite huge advances in both the sensors and artificial intelligence (AI) required to operate these cars, one thing has so far proved elusive: developing algorithms that can accurately and consistently identify objects, movements, and road conditions. As Mathew Monfort, a postdoctoral associate and researcher at the Massachusetts Institute of Technology (MIT) puts it: "An autonomous vehicle must actually function in the real world. However, it's extremely difficult and expensive to drive actual cars around to collect all the data necessary to make the technology completely reliable and safe." All of this is leading researchers down a different path: the use of game simulations and machine learning to build better algorithms and smarter vehicles.

"Killer Robots" worry the international community. From 13 to 17 November 2017, the Group of Governmental Experts on Lethal Autonomous Weapons Systems (LAWS), also familiarly designed as "killer robots" met for the first time in Geneva (UN Office at Geneva). LAWS are, broadly speaking, autonomous systems (robots) animated by artificial intelligence, which can kill without human decision. As stated in a preliminary paper, the creation of the Group shows an international concern "with the implications for warfare of a new suite of technologies including artificial intelligence and deep machine learning" (UNODA Occasional Papers No. 30, "Perspectives on Lethal Autonomous Weapon Systems" November 2017: 1). Are we, however, certain that AI will impact only LAWS? Or, rather, could AI impact much more than that, indeed, everything related to politics and geopolitics?

It is finally resonating with me that incorporating Deep Learning at the Edge has the potential to create a paradigm shift in the way robots are deployed in manufacturing operations. FANUC's aggressive move to integrated Deep Learning technologies could revolutionize the way robotic systems are deployed. When you consider how robots are deployed in manufacturing operations today, the application programs employ traditional procedural and function programming methods. But as robots increasingly rely upon vision systems to identify and locate geometric patterns on a work piece, the logic and decision making no longer has to be all pre-programmed in order to process the workpiece. Today, every robotic application program applies the experiential knowledge of a human expert to account for every possible situation that may arise in the manufacturing operation.

For R users, there hasn't been a production grade solution for deep learning (sorry MXNET). This post introduces the Keras interface for R and how it can be used to perform image classification. The post ends by providing some code snippets that show Keras is intuitive and powerful. Last January, Tensorflow for R was released, which provided access to the Tensorflow API from R. This was signficant, as Tensorflow is the most popular library for deep learning. However, for most R users, the Tensorflow for R interface was not very R like.

The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation. Building on two basic abstractions, it offers flexible building blocks for probabilistic computation. Distributions provide fast, numerically stable methods for generating samples and computing statistics, e.g., log density. Bijectors provide composable volume-tracking transformations with automatic caching. Together these enable modular construction of high dimensional distributions and transformations not possible with previous libraries (e.g., pixelCNNs, autoregressive flows, and reversible residual networks). They are the workhorse behind deep probabilistic programming systems like Edward and empower fast black-box inference in probabilistic models built on deep-network components. TensorFlow Distributions has proven an important part of the TensorFlow toolkit within Google and in the broader deep learning community.