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terça-feira, 28 de abril de 2020

Springer has released 65 Machine Learning and Data books for free

Springer has released 65 Machine Learning and Data books for free

The 65 books list:

The Elements of Statistical Learning
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Introductory Time Series with R
Paul S.P. Cowpertwait, Andrew V. Metcalfe
A Beginner’s Guide to R
Alain Zuur, Elena N. Ieno, Erik Meesters
Introduction to Evolutionary Computing
A.E. Eiben, J.E. Smith
Data Analysis
Siegmund Brandt
Linear and Nonlinear Programming
David G. Luenberger, Yinyu Ye
Introduction to Partial Differential Equations
David Borthwick
Fundamentals of Robotic Mechanical Systems
Jorge Angeles
Data Structures and Algorithms with Python
Kent D. Lee, Steve Hubbard
Introduction to Partial Differential Equations
Peter J. Olver
Methods of Mathematical Modelling
Thomas Witelski, Mark Bowen
LaTeX in 24 Hours
Dilip Datta
Introduction to Statistics and Data Analysis
Christian Heumann, Michael Schomaker, Shalabh
Principles of Data Mining
Max Bramer
Computer Vision
Richard Szeliski
Data Mining
Charu C. Aggarwal
Computational Geometry
Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark Overmars
Robotics, Vision and Control
Peter Corke
Statistical Analysis and Data Display
Richard M. Heiberger, Burt Holland
Statistics and Data Analysis for Financial Engineering
David Ruppert, David S. Matteson
Stochastic Processes and Calculus
Uwe Hassler
Statistical Analysis of Clinical Data on a Pocket Calculator
Ton J. Cleophas, Aeilko H. Zwinderman
Clinical Data Analysis on a Pocket Calculator
Ton J. Cleophas, Aeilko H. Zwinderman
The Data Science Design Manual
Steven S. Skiena
An Introduction to Machine Learning
Miroslav Kubat
Guide to Discrete Mathematics
Gerard O’Regan
Introduction to Time Series and Forecasting
Peter J. Brockwell, Richard A. Davis
Multivariate Calculus and Geometry
Seán Dineen
Statistics and Analysis of Scientific Data
Massimiliano Bonamente
Modelling Computing Systems
Faron Moller, Georg Struth
Search Methodologies
Edmund K. Burke, Graham Kendall
Linear Algebra Done Right
Sheldon Axler
Linear Algebra
Jörg Liesen, Volker Mehrmann
Serge Lang
Understanding Analysis
Stephen Abbott
Linear Programming
Robert J Vanderbei
Understanding Statistics Using R
Randall Schumacker, Sara Tomek
An Introduction to Statistical Learning
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
Statistical Learning from a Regression Perspective
Richard A. Berk
Applied Partial Differential Equations
J. David Logan
Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani, Giuseppe Oriolo
Regression Modeling Strategies
Frank E. Harrell , Jr.
A Modern Introduction to Probability and Statistics
F.M. Dekking, C. Kraaikamp, H.P. Lopuhaä, L.E. Meester
The Python Workbook
Ben Stephenson
Machine Learning in Medicine — a Complete Overview
Ton J. Cleophas, Aeilko H. Zwinderman
Object-Oriented Analysis, Design and Implementation
Brahma Dathan, Sarnath Ramnath
Introduction to Data Science
Laura Igual, Santi Seguí
Applied Predictive Modeling
Max Kuhn, Kjell Johnson
Python For ArcGIS
Laura Tateosian
Concise Guide to Databases
Peter Lake, Paul Crowther
Digital Image Processing
Wilhelm Burger, Mark J. Burge
Bayesian Essentials with R
Jean-Michel Marin, Christian P. Robert
Robotics, Vision and Control
Peter Corke
Foundations of Programming Languages
Kent D. Lee
Introduction to Artificial Intelligence
Wolfgang Ertel
Introduction to Deep Learning
Sandro Skansi
Linear Algebra and Analytic Geometry for Physical Sciences
Giovanni Landi, Alessandro Zampini
Applied Linear Algebra
Peter J. Olver, Chehrzad Shakiban
Neural Networks and Deep Learning
Charu C. Aggarwal
Data Science and Predictive Analytics
Ivo D. Dinov
Analysis for Computer Scientists
Michael Oberguggenberger, Alexander Ostermann
Excel Data Analysis
Hector Guerrero
A Beginners Guide to Python 3 Programming
John Hunt
Advanced Guide to Python 3 Programming
John Hunt