+DS IPLE: Scaling to Big Data
In machine learning, models are developed to represent and make predictions based on data. The model starts with random parameters and must "learn" these parameters by using historical data. In this class, we will discuss how learning is performed in practice, which is one of the key technical areas of machine learning. One of the big challenges in modern data science problems is the ability to perform such learning with massive datasets, so called "big data," with minimal human intervention. This lecture will include discussion of back-propagation, variants of stochastic gradient descent, and adaptive gradient methods.
This session is part of the Duke + Data Science (+DS) program in-person learning experiences (IPLEs). To learn more, please visit https://plus.datascience.duke.edu/