Practical Weak Supervision: Doing More with Less Data

Build products using deep learning, weakly supervised learning, and natural language processing without collecting millions of training records. This practical book explains how and provides a how-to guide for actually shipping deep learning models–since most of these projects never leave the lab. Deep networks have enabled new applications using unstructured data to proliferate, but much of the work means collecting millions of records as well as labeled datasets. Author Russell Jurney from Data Syndrome helps machine-learning engineers, software engineers, deep learning engineers, and data scientists learn practical applications using several weakly supervised learning methods. You'll explore: Semi-supervised learning: Combine a small amount of labeled data with a large amount of unlabeled data to train an improved final model Transfer learning: Re-train existing models from a related domain using training data from the problem domain Distant supervision: Combine low-quality labels from databases and other sources to create high-quality labels for the entire dataset Model versioning and management: start with a small labeled dataset and create a production grade model from concept through deployment