Remove 2016 Remove Interactive Remove Machine Learning Remove Predictive Modeling
article thumbnail

InfoTribes, Reality Brokers

O'Reilly on Data

Content creators and content consumers are connected, share information, and develop mental models of the world, along with shared or distinct realities, based on the information they consume. Online spaces are novel forms of community: people who haven’t met and may never meet in real life interacting in cyberspace.

article thumbnail

Tackling Bias in Machine Learning

Insight

Bias in Machine Learning Algorithms (Bottom Photos Source: ProPublica ; Top Photos Source: Pexels.com) Biases in predictive modeling are a widespread issue Machine learning and AI applications are used across industries, from recommendation engines to self-driving cars and more.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

A Window Into the Future of Data in Motion and What It Means for Businesses

CIO Business Intelligence

Despite this, only a handful of organisations interact with all stages of the data life cycle process to truly distill information that distinguishes future-ready businesses from the rest. Around 2016, we started talking about data in motion within the context of an enterprise data platform.

IoT 98
article thumbnail

A Window Into the Future of Data in Motion and What It Means for Businesses

Cloudera

Despite this, only a handful of organisations interact with all stages of the data life cycle process to truly distill information that distinguishes future-ready businesses from the rest. Around 2016, we started talking about data in motion within the context of an enterprise data platform.

IoT 98
article thumbnail

Techniques for Collecting, Prepping, and Plotting Data: Predicting Social Media-Influence in the NBA

Domino Data Lab

As Domino is committed to supporting data scientists and accelerating research, we reached out to Addison-Wesley Professional (AWP) Pearson for the appropriate permissions to excerpt “Predicting Social-Media Influence in the NBA” from the book, Pragmatic AI: An Introduction to Cloud-Based Machine Learning by Noah Gift.

article thumbnail

Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade.

Modeling 139
article thumbnail

Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

With the SG architecture, context words are predicted given the target word. Note: In more technical machine learning terms, the cost function of the skip-gram architecture is to maximize the log probability of any possible context word from a corpus given the current target word.] Natural Language Processing.] Joulin, A.,