In the business world, partnerships on an equal footing like these are part of the key to success in competitive and fast-moving environments. They bring access to expertise, more effective products and services, and greater potential for innovation and stability. Earlier this year, SAP and NVIDIA expanded their collaboration to create business applications based on artificial intelligence. Now, as NVIDIA's GPU Technology Conference kicks off in Munich, Germany, the partnership has gained even further substance. SAP installed its first NVIDIA DGX-1 systems – the world's first AI supercomputer – in Israel and Potsdam in 2016.

I've been trying for some time to replicate the results of the Bayesian Recurrent Neural Networks paper from Deepmind (https://arxiv.org/abs/1704.02798, the simple model without posterior sharpening), however my perplexity never reaches the result they have in the paper - 78.8 on validation, 75.5 on test set. I wanted to ask if anyone could spare a few minutes to take a look at my implementation and point out if anything is wrong. I've done an extensive hyperparameter search over various initialization schemes, learning rate, etc but haven't gotten close to their results. I'm using \pi 0.25, log \sigma_1 -1.0, log \sigma_2 -8.0 for the prior, and everything else should be like the paper states. Full implementation is here, and the exact file with the model is here.

There are six snippets of code that made deep learning what it is today. This article covers the inventors and the background to their breakthroughs. Each story includes simple code samples on FloydHub and GitHub to play around with. To run the code examples on FloydHub, make sure you have installed the floyd command line tool and cloned the code examples I've provided to your local machine. If you are new to FloydHub, you might want to first read the getting started with FloydHub section in my earlier post.

Welcome to part 4 of this series on deep learning. As you might have noticed there has been a slight delay between the first three entries and this post. The initial goal of this series was to write along with the fast.ai However, the concepts of the later lectures are often overlapping so I decided to finish the course first. This way I get to provide a more detailed overview of these topics. In this blog I want to cover a concept that spans multiple lectures of the course (4–6) and that has proven very useful to me in practice: Embedding Layers.

As described in the Part 1, I wanted to deploy my Deep Learning model into production. I've shown how to prepare the model for TensorFlow Serving. We exported the GAN model as Protobuf and it is now ready to be hosted. TensorFlow Serving implements a server that processes incoming requests and forwards them to a model. This server could be running somewhere, most probably, at your Cloud provider (such as Amazon AWS, Google Cloud Platform, Microsoft Azure), to be available to the world.

Fujitsu today announces the addition of nVidia Volta Graphical Processing Units (GPUs) to accelerate advances in artificial intelligence and support deep learning processing on its latest Primergy x86 servers. Available to customers in Europe, the Middle East, India and Africa beginning December 2017, select Primegy models are certified for the new-generation of nVidia Tesla V100 GPU accelerators. AI and deep learning computing involves large amounts of raw data and highly demanding compute environments. Fujitsu is rising to this challenge by introducing native deep learning processing capabilities to select Fujitsu Primergy CX and RX server models. To achieve the highest possible levels of system performance, Fujitsu is introducing native support for NVIDIA GPUs via direct connection to the mainboard.

NEW YORK – SoftBank Group Corp. is leading a $93 million (about ¥10.4 billion) investment in a startup that simplifies for companies the use of machine learning or deep learning applications at scale. The investment from a SoftBank subsidiary will be used to expand Petuum's team and further develop its operating system for specific industries, including manufacturing and health care, the company said Tuesday in a statement. "This technology should be standardized, accessible, and mass-producible, so that all can benefit from artificial intelligence, machine learning and deep learning," said Petuum founder and Chief Executive Officer Eric Xing. Its founders include Xing, an artificial intelligence professor and associate head of Carnegie Mellon University's Machine Learning department, as well as Qirong Ho, an adjunct assistant professor at the Singapore Management University School of Information Systems and Ning Li, a former advanced technology manager at Seagate Technology.

Con artistry is one of the world's oldest and most innovative professions, and it may soon have a new target. Research suggests artificial intelligence may be uniquely susceptible to tricksters, and as its influence in the modern world grows, attacks against it are likely to become more common. The root of the problem lies in the fact that artificial intelligence algorithms learn about the world in very different ways than people do, and so slight tweaks to the data fed into these algorithms can throw them off completely while remaining imperceptible to humans. Much of the research into this area has been conducted on image recognition systems, in particular those relying on deep learning neural networks. These systems are trained by showing them thousands of examples of images of a particular object until they can extract common features that allow them to accurately spot the object in new images.

SAN FRANCISO: Software giant Microsoft has announced its Project Brainwave deep learning acceleration platform for real-time artificial intelligence (AI). With the help of ultra-low latency, the system processes requests as fast as it receives them. "Real-time AI is becoming increasingly important as cloud infrastructures process live data streams, whether they be search queries, videos, sensor streams, or interactions with users," said Doug Burger, an engineer at Microsoft, in a blog post late on Tuesday. The'Project Brainwave' uses the massive field-programmable gate array (FPGA) infrastructure that Microsoft has been deploying over the past few years. "By attaching high-performance FPGAs directly to our datacentre network, we can serve DNNs as hardware microservices, where a DNN can be mapped to a pool of remote FPGAs and called by a server with no software in the loop," Burger said.

While we are still'wow'ing the early applications of machine learning technology, it continues to evolve at a fast pace, introducing us to more advanced algorithms and branches such as Deep Learning. Deep learning uses algorithms known as Neural Networks, which are inspired by the way biological nervous systems, such as the brain, to process information. It enables computers to identify every single data of what it represents and learn patterns. The primary software tool of deep learning is TensorFlow. It is an open source artificial intelligence library, using data flow graphs to build models.