Deep learning is a type of machine learning that’s growing at an almost frightening pace. Nearly every projection has the deep learning industry expanding massively over the next decade. This market research report, for example, expects deep learning to grow 71x in the US and more than that globally over the next ten years. There’s never been a better time than now to get started.

In a previous post, Ben Snively and Viral Desai showed us how to build a social media dashboard using serverless technology. The social media dashboard reads tweets with the #AWS hashtag, uses machine learning based services to do translation, and natural language processing (NLP) to determine topics, entities, and sentiment analysis. Finally, it aggregates this information using Amazon Athena and builds dashboards to visualize the information captured from the tweets. In this architecture, the only server to manage is running the application that reads the Twitter feed. In this blog post we’ll walk you through the steps to move this application to a Docker container and execute it in Amazon ECS with AWS Fargate. This removes the need to manage any Amazon EC2 instances in the architecture.

We at STATWORX work a lot with R and we often use the same little helper functions within our projects. These functions ease our daily work life by reducing repetitive code parts or by creating overviews of our projects. At first, there was no plan to make a package, but soon I realised, that it will be much easier to share and improve those functions, if they are within a package. Up till the 24th December I will present one function each day from helfRlein. So, on the 6th day of Christmas my true love gave to me…

Now there are a multitude of data science resources out there, all of whom claim to be the “best possible introductory to advanced material and courseware on the subject of data science”. Now I’ve made mistakes in choosing my data science references to buy and keep (and use) but I’ll be sharing what I’ve learned through experience to be the most effective for these particular topics. This list is both effective and born out of experience by going through them one at a time. The list of resources contains the following items:

1. Programming

Fairness-aware Generative Adversarial Network (FairGAN) Fairness-aware learning is increasingly important in data mining. Discrimination prevention aims to prevent discrimination in the training data before it is used to conduct predictive analysis. In this paper, we focus on fair data generation that ensures the generated data is discrimination free. Inspired by generative adversarial networks (GAN), we present fairness-aware generative adversarial networks, called FairGAN, which are able to learn a generator producing fair data and also preserving good data utility. Compared with the naive fair data generation models, FairGAN further ensures the classifiers which are trained on generated data can achieve fair classification on real data. Experiments on a real dataset show the effectiveness of FairGAN. …

By Jekaterina Kokatjuhha, Research Engineer at Zalando.

code2vec

Now, the P-value SneetchesHad tables with stars.The Bayesian SneetchesHad none upon thars.