While working through the recent animated pyramid chart tutorial, I came across a chart showing the shrinking surplus of women in Germany by Lisa Charlotte Rost. Taking the difference between men and women populations by age, she highlighted whether there were more men or more women for various age groups over time.

My current position is Principal Data Scientist and Executive Advisor at global technology consulting firm Booz Allen Hamilton. In this role, I am frequently asked many questions about the work we are doing, specifically with the U.S. federal government. I always defer those types of questions to open source materials, including articles, press releases, and content published on my company’s website.

Check this out! RStudio now has special features for Stan:

At: University of San Francisco Location: San Francisco, CAWeb: www.usfca.eduPosition: Assistant Professor, Tenure Track, Mathematics and Statistics

People are using Alexa for all types of activities in their homes, such as checking their bank balances, ordering pizza, or simply listening to their music from their favorite artists. For the most part, the primary interaction with the Echo has been your voice. In this blog post, we’ll show you how to build a new Alexa skill that will integrate with AWS DeepLens so when you ask “Alexa, what do you see?” Alexa returns objects detected by the AWS DeepLens device.

With the transition of facial expression recognition (FER) from laboratory-controlled to challenging in-the-wild conditions and the recent success of deep learning techniques in various fields, deep neural networks have increasingly been leveraged to learn discriminative representations for automatic FER. Recent deep FER systems generally focus on two important issues: overfitting caused by a lack of sufficient training data and expression-unrelated variations, such as illumination, head pose and identity bias. In this paper, we provide a comprehensive survey on deep FER, including datasets and algorithms that provide insights into these intrinsic problems. First, we describe the standard pipeline of a deep FER system with the related background knowledge and suggestions of applicable implementations for each stage. We then introduce the available datasets that are widely used in the literature and provide accepted data selection and evaluation principles for these datasets. For the state of the art in deep FER, we review existing novel deep neural networks and related training strategies that are designed for FER based on both static images and dynamic image sequences, and discuss their advantages and limitations. Competitive performances on widely used benchmarks are also summarized in this section. We then extend our survey to additional related issues and application scenarios. Finally, we review the remaining challenges and corresponding opportunities in this field as well as future directions for the design of robust deep FER systems. Deep Facial Expression Recognition: A Survey

Starting today, recreational weed is legal in Canada. This news has some how lead me to find Cannibus Curve, a mathmatical equation to draw Cannibus….!!!

Are you looking for some more information on building a data warehouse? The Intellixus website is the place you need to be today. A dedicated individual called Claude set up this blog in order to share some of his valuable knowledge he has learned over the years with each of his readers. He has a wealth of experience and has been working within this industry since 1996, initially working with SQL Server version 4.2. He is always happy to help and wanted to share his knowledge with more people, no matter whether he has had a chance to meet them or not.

Working Examples for Reproducible Research Documents (stationery)Templates, guides, and scripts for writing documents in ‘LaTeX’ and ‘R markdown’ to produce guides, slides, and reports. Special care is taken to illus …