Azure Machine Learning with Power BI SandDance Visualization


Power BI is the self-service BI engine of Microsoft, this service provides ability for business users to work with data with easier tools and be able to do changes or build their own models on top of one or more data sources. This is a full day of Power BI with many live demos. Training starts with Power BI and its components (Power Query, Power Pivot, Power View, Power Map, and Power Q&A). Course continues with real world demos of fetching data and transforming it with Power Query, loading and Modeling in Power Pivot and DAX, Visualizing it in Power BI Desktop and enhanced usage of Power Q&A. Many tips and best practices for using each component will be explained as well.

Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells Machine Learning

We propose a closed-loop power control algorithm for the downlink of the voice over LTE (VoLTE) radio bearer for an indoor environment served by small cells. The main contributions of our paper are: 1) proposing closed-loop power control for downlink VoLTE (or any packetized voice bearer), 2) deriving an upper bound of the loss in VoLTE downlink signal to noise plus interference ratio which the closed-loop power control has to overcome, 3) employing reinforcement learning to perform closed-loop power control, and 4) showing that this closed-loop power control method can improve the quality of VoLTE in a realistic network setup. Our simulation results have shown that our proposed algorithm significantly improved both voice retainability and mean opinion score as a result of maintaining the effective downlink signal to interference plus noise ratio against adverse network operational issues and faults.

Use Power BI API with service principal (Preview) Machine Learning Analytikus United States


Service principal is a local representation of your AAD application for use in a specific tenant and will allow you to access resources or perform operations using Power BI API without the need for a user to sign in or have a Power BI Pro license. For customers using Power BI Embedded it can significantly reduce other limitations and friction.

A Deep Generative Model for Graphs: Supervised Subset Selection to Create Diverse Realistic Graphs with Applications to Power Networks Synthesis Machine Learning

Creating and modeling real-world graphs is a crucial problem in various applications of engineering, biology, and social sciences; however, learning the distributions of nodes/edges and sampling from them to generate realistic graphs is still challenging. Moreover, generating a diverse set of synthetic graphs that all imitate a real network is not addressed. In this paper, the novel problem of creating diverse synthetic graphs is solved. First, we devise the deep supervised subset selection (DeepS3) algorithm; Given a ground-truth set of data points, DeepS3 selects a diverse subset of all items (i.e. data points) that best represent the items in the ground-truth set. Furthermore, we propose the deep graph representation recurrent network (GRRN) as a novel generative model that learns a probabilistic representation of a real weighted graph. Training the GRRN, we generate a large set of synthetic graphs that are likely to follow the same features and adjacency patterns as the original one. Incorporating GRRN with DeepS3, we select a diverse subset of generated graphs that best represent the behaviors of the real graph (i.e. our ground-truth). We apply our model to the novel problem of power grid synthesis, where a synthetic power network is created with the same physical/geometric properties as a real power system without revealing the real locations of the substations (nodes) and the lines (edges), since such data is confidential. Experiments on the Synthetic Power Grid Data Set show accurate synthetic networks that follow similar structural and spatial properties as the real power grid.

A powerful cognitive and deep learning tool - IBM Systems Blog: In the Making


From time to time, we invite industry thought leaders to share their opinions and insights on current technology trends to the In The Making blog. Getting the right tool for the job is essential for anything from home improvement projects to launching satellites. I view the new trend of applying AI, deep learning and cognitive techniques to enterprise IT solutions as following that basic principle. Some tools are more complex and difficult to create than others, but they should all be viewed as a means to an end, not the end in itself. A recent, stunning announcement by IBM is a great example of what I mean.