Posts by tag

Predictions

3 posts
View Post

5 Ways Digital Business Will Change In 2021

COVID-19 made what was once optional into a mandatory part of business strategy, and 2021 will only move the needle further to the digital side, Forrester predicts. Forrester has released its predictions for digital business in 2021, and it sees big changes coming as the post-pandemic era continues to rapidly transform the business landscape. “What many considered optional became imperative overnight,” Forrester said of digital business, citing that one retailer saw five years’ worth of digital adoption happen in only five months due to COVID-19 lockdowns. 2021 is the year, Forrester predicts, the majority of businesses will invest in having a full-fledged digital…
View Post
Share
View Post

Machine Learning Could Improve Hurricane Prediction

Applying a machine learning technique to a group of possible storm paths could help meteorologists provide more accurate medium-term hurricane forecasts. This approach could also help them issue timely warnings to communities in the path of these potentially deadly storms, report researchers. In a new study, the researchers used machine learning to remove certain groups of hurricane predictions from ensembles—sets of predictions from weather models that are based on a range of weather possibilities—to lower errors and improve forecasts four to six days ahead. “…WHEN YOU ARE FACING A HURRICANE, IT’S IMPORTANT TO KNOW WHAT TYPE OF STORM YOU’RE GOING…
View Post
Share
View Post

TF Dev Summit ’19 | TensorFlow Probability: Learning With Confidence

TF Dev Summit ’19 | TensorFlow Probability: Learning with confidence TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). It’s for data scientists, statisticians, and ML researchers/practitioners who want to encode domain knowledge to understand data and make predictions with uncertainty estimates. In this talk we focus on the “layers” module and demonstrate how TFP “distributions” fit naturally with Keras to enable estimating aleatoric and/or epistemic uncertainty. Speaker: Josh Dillon, Software Engineer
View Post
Share