By providing a scalable cloud platform for building communications experiences, Twilio enables developers and businesses to build any customer engagement into their applications using simple and powerful APIs for voice, messaging, and video. Businesses like Morgan Stanley, Marks & Spencer, Netflix, Lyft, Airbnb, and more than 50,000 others are modernizing the way they communicate with their customers using the Twilio platform.

Many real-world applications involve code with output that contains a certain degree of randomness. Consider, for example, code for the analysis of measurements. Measurements typically result in data randomly distributed according to some well-defined distribution. The task for the analysis code may then involve statistical analyses, the result of which is, again, randomly distributed according to some distribution.

The Amazon SageMaker pre-built MXNet container now uses the latest release of Apache MXNet 1.2.  Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.  And the pre-built MXNet container makes it easy to write your deep learning scripts naturally but still take advantage of distributed, managed training and real-time production hosting in Amazon SageMaker.

In Pandas, one can easily apply operations on all the data using the apply method. However, this method is quite slow and is not useful when scaling up your methods. Is there a way to speed up these operations? And if so, how? Yes, there is! This blog post will explain how you can use Dask to maximize the power of parallelization and to scale out your DataFrame operations.

Someone pointed me to this post from a few years ago where Rafael Irizarry argues that scientific “pessimists” such as myself are, at least in some fields, “missing a critical point: that in practice, there is an inverse relationship between increasing rates of true discoveries and decreasing rates of false discoveries and that true discoveries from fields such as the biomedical sciences provide an enormous benefit to society.” So far so good—within the framework in which the goal of p-value-style science is to make “discoveries” and in which these discoveries can be characterized as “true” or “false.”

Not a full R article, but a quick note demonstrating by example the advantage of being able to collect many expressions and pack them into a single extend_se() node.

Optimally Robust Estimation for Extreme Value Distributions (RobExtremes)Optimally robust estimation for extreme value distributions using S4 classes and methods (based on packages ‘distr’, ‘distrEx’, ‘distrMod’, ‘RobAStBase …

Random Subsampling Random sub­sampling, which is also known as Monte Carlo crossvalidation, as multiple holdout or as repeated evaluation set, is based on randomly splitting the data into subsets, whereby the size of the subsets is defined by the user. The random partitioning of the data can be repeated arbitrarily often. In contrast to a full crossvalidation procedure, random subsampling has been shown to be asymptotically consistent resulting in more pessimistic predictions of the test data compared with crossvalidation. The predictions of the test data give a realistic estimation of the predictions of external validation data . …

“What is a Prediction Problem? A business problem which involves predicting future events by extracting patterns in the historical data. Prediction problems are solved using Statistical techniques, mathematical models or machine learning techniques. For example: Forecasting stock price for the next week, predicting which football team wins the world cup, etc.” Suresh Kumar Gorakala ( December 26, 2014 )