Windows 10 after two years: Was the upgrade worth it? After a little more than two years, Microsoft has finally settled into a rhythm with its new, fast-paced development cadence for Windows 10. Check Settings System About to see full details about the current Windows 10 installation. What Microsoft's marketers are calling the Fall Creators Update (officially version 1709) begins arriving on desktop PCs today via Windows Update and will soon be available for download at all the usual places. The final build number for this release is 16299.
NVIDIA's meteoric growth in the datacenter, where its business is now generating some $1.6B annually, has been largely driven by the demand to train deep neural networks for Machine Learning (ML) and Artificial Intelligence (AI)--an area where the computational requirements are simply mindboggling. First, and perhaps most importantly, Huang announced new TensorRT3 software that optimizes trained neural networks for inference processing on NVIDIA GPUs. In addition to announcing the Chinese deployment wins, Huang provided some pretty compelling benchmarks to demonstrate the company's prowess in accelerating Machine Learning inference operations, in the datacenter and at the edge. In addition to the TensorRT3 deployments, Huang announced that the largest Chinese Cloud Service Providers, Alibaba, Baidu, and Tencent, are all offering the company's newest Tesla V100 GPUs to their customers for scientific and deep learning applications.
This is a programming oriented, hands-on training for starting a career in Data Mining and Machine Learning, and to acquire the necessary skills in statistical and inferential thinking. After this course, many of the things you read and hear about Data Science, Artificial Intelligence and Machine learning would make a lot more sense. The applications of this field span from marketing analysis and forecasts, predicting demands for products, making intelligent business decisions, cyber security and threat detection, predicting poll and survey results, and too many others to mention here. This course will enable participants to learn the foundation skills through programming, in arguably the most popular Data Science language today--Python.
With the help of ultra-low latency, the system processes requests as fast as it receives them. He added that the system architecture reduces latency, since the CPU does not need to process incoming requests, and allows very high throughput, with the FPGA processing requests as fast as the network can stream them. Microsoft is also planning to bring the real-time AI system to users in Azure. "With the'Project Brainwave' system incorporated at scale and available to our customers, Microsoft Azure will have industry-leading capabilities for real-time AI," Burger noted.
Second, framework developers need to maintain multiple backends to guarantee performance on hardware ranging from smartphone chips to data center GPUs. Diverse AI frameworks and hardware bring huge benefits to users, but it is very challenging to AI developers to deliver consistent results to end users. Motivated by the compiler technology, a group of researchers including Tianqi Chen, Thierry Moreau, Haichen Shen, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy from Paul G. Allen School of Computer Science & Engineering, University of Washington, together with Ziheng Jiang from the AWS AI team, introduced the TVM stack to simplify this problem. Today, AWS is excited to announce, together with the research team from UW, an end-to-end compiler based on the TVM stack that compiles workloads directly from various deep learning frontends into optimized machine codes.
Hadoop is the open-source version of Google's Map/Reduce and Google File System (GFS), widely used for large data-crunching applications. It is a shared-nothing cluster, which means that as you add cluster nodes, performance scales up smoothly. In the paper, Performance of a Low Cost Hadoop Cluster for Image Analysis, researchers Basit Qureshia, Yasir Javeda, Anis Kouba, Mohamed-Foued Sritic, and Maram Alajlan, built a 20 node RPi Model 2 cluster, brought up Hadoop on it, and used it for surveillance drone image analysis. The team ran a series of tests that were a) compute-intensive (calculating Pi), b) I/O intensive (document word counts), and, c) both (large image file pixel counts).
Specifically, it was engineered to exploit every bit of memory and hardware resources for tree boosting algorithms. The implementation of XGBoost offers several advanced features for model tuning, computing environments and algorithm enhancement. It is capable of performing the three main forms of gradient boosting (Gradient Boosting (GB), Stochastic GB and Regularized GB) and it is robust enough to support fine tuning and addition of regularization parameters. XGBoost specifically, implements this algorithm for decision tree boosting with an additional custom regularization term in the objective function.
In this post, I share an AutoML setup to train and deploy pipelines in the cloud using Python, Flask, and two AutoML frameworks that automate feature engineering and model building. I tested and combined two open source Python tools: tsfresh, an automated feature engineering tool, and, TPOT, an automated feature preprocessing and model optimization tool. After an optimal feature engineering and model building pipeline is determined, our pipeline is persisted within our Flask application within a Python dictionary–the dictionary key being the pipeline id specified in the parameter file. I have shown how to make use of open source AutoML tools and operationalize a scalable automated feature engineering and model building pipeline to the cloud.
YellowHead has launched Alison, a machine learning technology that predicts how mobile advertising campaigns, known as paid user acquisition, will turn out. It specializes in paid user acquisition campaigns, app store optimization, and search engine optimization. And now it has added Alison to use machine learning to predict a campaign's performance in the hopes of uncovering more insights for brands and wasting less advertising money. Top university math professors at the Data Science Research Team at Tel Aviv University and the company's developers worked on Alison, which supplements human intelligence to optimize campaigns based on predicted results across multiple ad platforms such as Facebook and Google.
By modeling human testers, including manual and test automation tasks such as scripting, Appvance has developed algorithms and expert systems to take on those tasks, similar to how driverless vehicle software models what a human driver does. The Appvance AI technology learns from various existing data sources, including learning to map an application fully on its own, various server logs, Splunk or Sumo Logic production data, form input data, valid headers and requests, expected responses, changes in each build and others. The resulting test execution represented real user flows, data driven, with near 100% code coverage. Built from the ground up with DevOps, agile and cloud services in mind, Appvance offers true beginning-to-end data-driven functional, performance, compatibility, security and synthetic APM test automation and execution, enabling dev and QA teams to quickly identify issues in a fraction of the time of other test automation products.