Corinium

JUNE 6, 2019

It is also important to have a strong test and learn culture to encourage rapid experimentation. A playbook for this is to run multiple experiments in parallel and create ‘MVPs’ (fail/learn fast), as well as incorporate feedback mechanisms to enable an improvement loop, and scaling the ones that show the fastest path to ROI.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

O'Reilly on Data

APRIL 19, 2021

We’ll look at this later, but being able to reproduce experimental results is critical to any science, and it’s a well-known problem in AI. In contrast, in our 2018 report, Asia was behind in mature practices, though it had a markedly higher number of respondents in the “early adopter” or “exploring” stages. Bottlenecks to AI adoption.

Enterprise 356

Enterprise Modeling Risk Manufacturing 356

Domino Data Lab

AUGUST 22, 2019

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. Introduction.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Domino Data Lab

MAY 8, 2019

2018-06-21). On the other hand, as Lipton emphasized, while the tooling produces interesting visualizations, visualizations do not imply interpretation. ML model interpretability and data visualization. Business stakeholders cannot rely on visualizations to interpret the ML models that their data science teams produce.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

O'Reilly on Data

SEPTEMBER 11, 2019

Our call for speakers for Strata NY 2019 solicited contributions on the themes of data science and ML; data engineering and architecture; streaming and the Internet of Things (IoT); business analytics and data visualization; and automation, security, and data privacy. 84 this year, it’s down 20 places from 2018, and down 23 from 2017.)

IoT 20

IoT Big Data Data Warehouse Uncertainty 20