We’re now less than five months away from the required compliance date of the EU General Data Protection Regulation (GDPR). Organizations that have EU residents as customers, suppliers or partners are required to be in compliance by May 2018. For legislation that has received so much publicity, it’s surprising how unprepared many organizations are for the coming changes. The first step in the process is to gain a clear view of all the personal data the organization holds and where it is. This blog will look at the obligations for working with data under GDPR and how Data Discovery is essential to build a foundation for GDPR-compliant data management. The EU provided a two-year transition period for the implementation of GDPR but, in truth, few companies have used this time effectively. The Compliance, Governance and Oversight Council (CGOC) has found that only 6% of global companies believe they are prepared for GDPR. In November last year, research showed that 92% of European businesses said they were unprepared for GDPR. The fact that almost 30% of these companies admitted they were unfamiliar with the regulation is even more surprising – especially given that fines for data breaches can run to €20 million or 4% of annual turnover. Companies that have yet to prepare for GDPR must start immediately. Organizations may not be fully compliant by May 2018 but they need to be able to demonstrate that they are making best efforts in that direction. Implementing a sound Data Discovery strategy is an extremely good starting point as responsibilities for managing personal data have grown dramatically.
Integrating R Notebooks and R shiny with Tableau enables us to take advantage of the various statistical analysis and machine learning packages in R. In this short blog post, we will see how to integrate Tableau with R through R Notebooks and shiny. This approach helps us to have descriptive, inferential and predictive analytics in our Tableau story/dashboard. The data I am using is reading test from the Program for International Student Assessment (PISA). Please check out the video tutorial below.
This guide serves as a basic hands-on work to lead you through building a neural network from scratch. Most of the mathematical concepts and scientific decisions are left out. You are free to research more on that part.
Capsule Networks (CapsNets) are a hot new neural net architecture that may well have a profound impact on deep learning, in particular for computer vision. Wait, isn’t computer vision pretty much solved already? Haven’t we all seen fabulous examples of convolutional neural networks (CNNs) reaching super-human level in various computer vision tasks, such as classification, localization, object detection, semantic segmentation or instance segmentation?