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mso-bidi-theme-font: minor-latin;">It is common for machine learning practitioners to pick up missing bits and pieces of linear algebra and optimization via “osmosis” while …

Bayesian optimization is a methodology for optimizing expensive objective functions that has proven success in the sciences, engineering, and beyond. This timely text provides a …

The book trains readers to break down various ML applications and methods in terms of data, model, and loss, thus helping them to choose from the vast range of ready-made ML …

‘An engaging, insightful and panoramic survey of where we are, why we got here and what it means. A brilliant guide to the most important technology of our times’ Mustafa Suleyman, …

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.Gaussian …

More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to …

Maxim Lapan delivers intuitive explanations and insights into complex reinforcement learning (RL) concepts, starting from the basics of RL on simple environments and tasks to …

NATIONAL BESTSELLER - Bold futurist Ray Kurzweil, author of The Singularity Is Near, offers a framework for envisioning the future of machine intelligence--"a book for anyone who …