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Book Review: Machine Learning with PyTorch and Scikit-Learn

The enticing new title courtesy of Packt Publishing, “Machine Learning with PyTorch and Scikit-Learn,” by Sebastian Raschka, Yuxi (Hayden) Liu, and Vahid Mirjalili is a welcome addition to any data scientist’s list of learning resources. This 2022 tome consists of 741 well-crafted pages designed to provide a comprehensive framework for working in the realm of machine learning and deep learning. The book is brimming with topics that will propel you to a leading-edge understanding of the field.

Interview: Global Technology Leader PNY

The following whitepaper download is a reprint of the recent interview with our friends over at PNY to discuss a variety of topics affecting data scientists conducting work on big data problem domains including how “Big Data” is becoming increasingly accessible with big clusters with disk-based databases, small clusters with in-memory data, single systems with in-CPU-memory data, and single systems with in-GPU-memory data. Answering our inquiries were: Bojan Tunguz, Senior System Software Engineer, NVIDIA and Carl Flygare, NVIDIA Quadro Product Marketing Manager, PNY.