Linear Algebra for Data Science, Machine Learning, and Signal Processing
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Mathematical Introduction to Data Science
Sven A. Wegner
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Enumerative Combinatorics: Volume 2
Richard (Massachusetts Institute of Technology) Stanley
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Modern Fortran Explained : Incorporating Fortran 2023
Mr Michael (Formerly of CERN, Geneva, Switzerland) Metcalf
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Reflections on the Foundations of Probability and Statistics : Essays in Honor of Teddy Seidenfeld
Thomas Augustin
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All the Math You Missed : (But Need to Know for Graduate School)
Thomas A. (Williams College, Massachusetts) Garrity
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Thomas A. Garrity
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Thomas A. Garrity
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Linear Algebra And Optimization With Applications To Machine Learning - Volume Ii: Fundamentals Of Optimization Theory With Applications To Machine Learning
Jean H (Univ Of Pennsylvania, Usa) Gallier
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The Nature of Complex Networks
Sergey N. (Researcher, Researcher, Department of Physics, Dorogovtsev
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Linear Algebra And Optimization With Applications To Machine Learning - Volume I: Linear Algebra For Computer Vision, Robotics, And Machine Learning
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Proven Impossible : Elementary Proofs of Profound Impossibility from Arrow, Bell, Chaitin, Godel, Turing and More
Dan (University of California, Davis) Gusfield
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Dan (University of California, Davis) Gusfield
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The 9 Pitfalls of Data Science
Gary (Fletcher Jones Professor of Economics, Fletcher Jones Pro Smith
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An Introduction to Python Programming for Scientists and Engineers
Johnny Wei-Bing Lin
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Mathematical Transformations and Wavelet Filters for Source Coding and Signal Processing Systems
William A. Pearlman
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Enumerative Combinatorics: Volume 2
Richard (Massachusetts Institute of Technology) Stanley
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Demystifying Deep Learning : An Introduction to the Mathematics of Neural Networks
Douglas J. (University of Kent, UK) Santry
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Modern Fortran Explained : Incorporating Fortran 2023
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Non-Bayesian Decision Theory : Beliefs and Desires as Reasons for Action
Martin Peterson
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Mathematical Modeling and Signal Processing in Speech and Hearing Sciences
Jack Xin
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