Loading...

Timeline of scalable machine learning & stochastic gradient

With the advent of the internet, the growth of social media, and the embedding of sensors in the world, the magnitudes of data that our machine learning algorithms must handle have grown tremendously over the last decade. This effect is sometimes called "Big Data". Thus, our learning algorithms must scale to bigger and bigger datasets. In this module, you will develop a small modification of gradient ascent called stochastic gradient, which provides significant speedups in the running time of our algorithms. This simple change can drastically improve scaling, but makes the algorithm less stable and harder to use in practice. In this module, you will investigate the practical techniques needed to make stochastic gradient viable, and to thus to obtain learning algorithms that scale to huge datasets. You will also address a new kind of machine learning problem, online learning, where the data streams in over time, and we must learn the coefficients as the data arrives. This task can also be solved with stochastic gradient. You will implement your very own stochastic gradient ascent algorithm for logistic regression from scratch, and evaluate it on sentiment analysis data.

À propos de Coursera

Cours, Spécialisations et Diplômes en ligne enseignés par des enseignants du plus haut niveau provenant des meilleurs universités et établissements d'enseignement du monde.

Community
Join a community of 40 million learners from around the world
Certificate
Earn a skill-based course certificate to apply your knowledge
Career
Gain confidence in your skills and further your career