See how you can connect devices to create powerful IoT applications on the flexible Azure IoT platform. See how Azure IoT simplifies IoT development--use your preferred language, tools and existing developer SDKs to start building IoT right away. Get started quickly with solution accelerators such as Remote Monitoring, Predictive Maintenance and Connected Factory for common IoT scenarios. Learn how to use Azure IoT Hub to easily create, customize, and manage all aspects of your IoT application, and IoT Edge to deploy cloud apps on the edge--creating more intelligent solutions, whether in the cloud or on-premises. Create visualizations of IoT time-series data and create insights from your IoT application with other Azure platform services.

WIRE)--Jul 30, 2018--MapR Technologies, Inc., provider of the industry's leading data platform for AI and Analytics, today announced a new, free introductory course from MapR Academy on Artificial Intelligence (AI) and Machine Learning (ML). This on-demand course provides insights into how businesses can leverage AI and ML to improve operations through real-world use cases. This course is ideal for developers just starting out in ML, as well as higher-level business decision makers. "Machine learning, a trending topic in big data, is not fully understood. This course provides a foundation for anyone curious about ML/AI and how it works in practical applications," said Suzanne Ferry, vice president, global training and enablement, MapR.

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Scientists are using artificial intelligence to build systems that can analyze the quality of classroom instruction and student engagement. School leaders and education researchers often rely on test scores to judge how well students are learning. But that ignores many important aspects of learning, such as the liveliness of classroom discussion or how engaged and motivated the students are. Expert observers in a classroom can immediately pick up on these unquantifiable moments of great teaching. But human observations are time-consuming and expensive.

While manufacturers have traditionally been hesitant to invest in their operations due to cost, a recent National Association of Manufacturers (NAM) survey of more than 500 manufacturers reveals that 65 percent plan to increase capital spending during the coming years. Where is the money going to go? Experts predict most manufacturers will look toward revamping their facilities to adapt to the demands of today's digital world. This adjustment, which many call the "fourth industrial revolution" or smart manufacturing, will move manufacturers from mass production to customized production via a digital supply network. The digital transformation, a melding of new technologies such as the internet of things (IoT), advanced robotics, artificial intelligence (AI), and 3D printing, is expected to generate more than $370 billion in net global value during the next four years. While digitization is helping to get more out of manufacturers' materials and machines, what are the driving forces behind transformation?

Machine learning has had the entire data science community brimming with questions regarding the framework and the hidden insights that are being used to build the intelligent applications of the future. There are immense possibilities when it comes to grasping the inner workings of algorithms related to machine learning. As mathematics is a big portion of the programming process, there are many important questions to ask and many reasons to include math in the machine learning development process. The knowledge of mathematics is very important to understand and apply machine learning algorithms in different applications. From understanding uncertainty and intervals of prediction to choose a parameter settings with a strategy for validation, mathematics concepts help in implementing machine learning.

Generative Adversarial Networks (GANs) can successfully learn a probability distribution and produce realistic samples. However, open questions such as sufficient convergence conditions and mode collapse still persist. In this paper, we build on existing work in the area by proposing a novel framework for training the generator against an ensemble of discriminator networks, which can be seen as a one-student/multiple-teachers setting. We formalize this problem within the non-stationary Multi-Armed Bandit (MAB) framework, where we evaluate the capability of a bandit algorithm to select discriminators for providing the generator with feedback during learning. To this end, we propose a reward function which reflects the amount of knowledge learned by the generator and dynamically selects the optimal discriminator network. Finally, we connect our algorithm to stochastic optimization methods and show that existing methods using multiple discriminators in literature can be recovered from our parametric model. Experimental results based on the Fr\'echet Inception Distance (FID) demonstrates faster convergence than existing baselines and show that our method learns a curriculum.

Universally valid ground truth is almost impossible to obtain or would come at a very high cost. For supervised learning without universally valid ground truth, a recommended approach is applying crowdsourcing: Gathering a large data set annotated by multiple individuals of varying possibly expertise levels and inferring the ground truth data to be used as labels to train the classifier. Nevertheless, due to the sensitivity of the problem at hand (e.g. mitosis detection in breast cancer histology images), the obtained data needs verification and proper assessment before being used for classifier training. Even in the context of organic computing systems, an indisputable ground truth might not always exist. Therefore, it should be inferred through the aggregation and verification of the local knowledge of each autonomous agent.

Coreference Resolution is the task of identifying groups of phrases in a text that refer to the same discourse entity. Such referring phrases are called mentions, a set of mentions that all refer to the same 1 discourse entity is called a coreference chain. Annotated corpora are important resources for developing and evaluating automatic coreference resolution methods. Turkish is an agglutinative language and Turkish coreference resolution poses several challenges different from many other languages, in particular the absence of grammatical gender, the possibility of null pronouns in subject and object position, possessive pronouns that can be expressed as suffixes, and ambiguities among possessive and number morphemes, e.g., 'çocukları' can be analysed as'their children' or as'his/her children', depending on context Oflazer and Bozşahin (1994). No coreference resolution corpus exists for Turkish so far. We here describe the result of an effort to create such a corpus based on the METU-Sabanci Turkish Treebank (Say, Zeyrek, Oflazer, and Özge, 2004; Atalay, Oflazer, and Say, 2003; Oflazer, Say, Hakkani-Tür, and Tür, 2003) which is, to the best of our knowledge, the only publicly available Turkish Treebank. Our contributions are as follows.