Distributed Machine Learning
Distributed Machine Learning is a scholarly work, published in 2018. The main subjects of the publication include machine learning, active learning, computer science, Graph matching, and artificial intelligence. Second, authors will introduce widely used ways of parallelizing machine learning algorithms (including both data parallelism and model parallelism, both synchronous and asynchronous parallelization), and discuss their theoretical properties, strengths, and weakness.