How to Develop a Reusable Framework to Spot-Check Algorithms in Python

Spot-checking algorithms is a technique in applied machine learning designed to quickly and objectively provide a first set of results on a new predictive modeling problem. Unlike grid searching and other types of algorithm tuning that seek the optimal algorithm or optimal configuration for an…

A Look Inside Facebook’s AI Machine – AI Trends

(L-R) Joaquin Candela, Director of Engineering for Applied Machine Learning; Manohar Paluri, Applied Computer Vision Team Lead; Rita Aquino, Technical Product Manager; and Rajen Subba, Engineering Manager. Photo by Stephen Lam By Steven Levy, Wired When asked to head Facebook’s Applied Machine Learning group — to…

How ML Keeps Shelves Stocked at Home Depot with Pat Woowong – TWiML Talk #175

Today we’re joined by Pat Woowong, principal engineer in the applied machine intelligence group at The Home Depot. We discuss a project that Pat recently presented at the Google Cloud Next conference which used machine learning to predict shelf-out scenarios within stores. We dig into…

Executive Interview: Dr. Foteini Agrafioti, Head of Borealis AI and Chief Science Officer, RBC – AI Trends

Combining Fundamental and Applied AI Research to Create Opportunity for Canada Dr. Agrafioti is the Chief Science Officer at RBC and Head of Borealis AI. She is responsible for RBC’s intellectual property portfolio in the fields of artificial intelligence and machine learning. Prior to joining…

Artificial Intelligence & the Economy | Making an AI-based malicious weed detection application in under a week

Artificial Intelligence and the Economy features machine-learning … highlight how machine learning/artificial intelligence can be applied for small … many types of artificial neural networks, such as Convolutional Artificial Neural Networks … VISIT THE SOURCE ARTICLE Author:

The Role of Randomization to Address Confounding Variables in Machine Learning

A large part of applied machine learning is about running controlled experiments to discover what algorithm or algorithm configuration to use on a predictive modeling problem. A challenge is that there are aspects of the problem and the algorithm called confounding variables that cannot be…

Nathan Kutz Interview – Machine Learning to Discover Physics and Engineering Principles

In this episode, I’m joined by Nathan Kutz, Professor of applied mathematics, electrical engineering and physics at the University of Washington. Nathan and I met a few months ago at the Prepare.AI conference in St. Louis where he gave a talk on “Machine Learning to…

Controlled Experiments in Machine Learning

Systematic experimentation is a key part of applied machine learning. Given the complexity of machine learning methods, they resist formal analysis methods. Therefore, we must learn about the behavior of algorithms on our specific problems empirically. We do this using controlled experiments. In this tutorial,…

Statistical Significance Tests for Comparing Machine Learning Algorithms

Comparing machine learning methods and selecting a final model is a common operation in applied machine learning. Models are commonly evaluated using resampling methods like k-fold cross-validation from which mean skill scores are calculated and compared directly. Although simple, this approach can be misleading as…

AI Making Real-Time Analytics More Real, Driving High Value – AI Trends

We’ve come a long way with analytics in recent years, in which data is applied against algorithms or analytics engines to determine what it may mean to the business. Lately, there’s been a lot of progress with real-time analytics, especially when applied against streaming data…