Scaling up Machine Learning Pdf This publication introduces an integrated collection of agent strategies for scaling up machine learning and data mining techniques on parallel and distributed computing systems. Requirement for parallelizing learning algorithms is exceptionally task-specific: in certain settings it’s driven by the tremendous dataset sizes, the others by design sophistication or by real time performance requirements. Creating task-appropriate platform and algorithm options for large-scale system learning necessitates understanding the advantages, trade-offs, and limitations of the available choices.
Solutions introduced at the book cover a variety of parallelization platforms from FPGAs and GPUs into multi-core systems and commodity clusters, concurrent programming frameworks such as CUDA, MPI, MapReduce, and DryadLINQ, and studying configurations (supervised, unsupervised, semi-supervised, and online learning). Extensive coverage of parallelization of trees that are fostered, SVMs, spectral clustering, belief propagation and other popular learning algorithms and deep dives into many programs make the book both helpful for researchers, students, and professionals.
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