Scale By the Bay 2019

Selecting the right training data for human review is known as Active Learning. Almost every company invents (or reinvents) the same Active Learning strategies and too often they repeat the same avoidable errors. This talk will share some common Active Learning strategies, with PyTorch examples, covering: Least Confidence Sampling, Entropy-based Sampling, Cluster-based Sampling, Model-based Outliers, Monte Carlo Dropouts (Deep Bayesian Active Learning), Representative Sampling, and Sampling for Real-World Diversity.