Springer открыл бесплатный доступ к 65 книгам по машинному обучению и данным

Автор Uri Eliabayev

Сотни книг теперь можно скачать бесплатно

Springer открыл доступ к сотням книг по широкому кругу тем для широкой публики. Список, включающий в себя 408 книг, охватывает широкий спектр научных и технологических тем. Здесь создан список всех книг (всего 65), относящихся к области данных и машинного обучения.

Среди книг вы найдете книги, посвященные математической части предметной области (алгебра, статистика и т. д.), а также более продвинутые книги по глубокому обучению и другим сложным темам. Вы также можете найти несколько хороших книг на разных языках программирования, таких как Python, R, MATLAB и т. д.

Если вы ищете больше рекомендуемых книг о машинном обучении и данных, вы можете проверить мою предыдущую статью об этом.

Список 65 книг:

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

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