摘要： Here are some articles from Microsoft regarding cloud data science products and updates. This week includes IoT hubs, Time Series Insights, Deep Learning Virtual Machine, Python sample code for cognitive services, and more.
摘要： Latent Profile Analysis (LPA) tries to identify clusters of individuals (i.e., latent profiles) based on responses to a series of continuous variables (i.e., indicators). LPA assumes that there are unobserved latent profiles that generate patterns of responses on indicator items.
摘要： In this article we’re going to take a look at the 3 most common loss functions for Machine Learning Regression. I’ll explain how they work, their pros and cons, and how they can be most effectively applied when training regression models.
Summary： Outlining some of the common pitfalls of machine learning for time series forecasting, with a look at time delayed predictions, autocorrelations, stationarity, accuracy metrics, and more.
