Springer가 무료로 공개한 기계학습 도서

1.
SpringerLink SpringerLink라고 아시나요? 수백만권의 과학저서를 온라인으로, 무료로 제공해주는 사이트입니다. 넓은 의미로 보면 Open Access입니다. 혹 Quantpedia를 아시나요?에서 소개하였고 많은 분들이 이용하고 계시는 arXiv, SSRN과 같은 서비스입니다. Springer가 정의하는 Open Access는 다음과 같습니다.

What is open access?

Open access publications are freely and permanently available online to anyone with an internet connection. Unrestricted use, distribution and reproduction in any medium is permitted, provided the author/editor is properly attributed.

As such, every article appearing in any SpringerOpen journal and any book published with SpringerOpen is ‘open access’, meaning that:

The article/book is universally and freely accessible via the Internet, in an easily readable format. All publications are deposited immediately upon publication, without embargo, in an agreed format – current preference is XML with a declared DTD – in at least one widely and internationally recognised open access repository.
The author(s) or copyright owner(s) irrevocably grant(s) to any third party, in advance and in perpetuity, the right to use, reproduce or disseminate the article/book in its entirety or in part, in any format or medium, provided that no substantive errors are introduced in the process, proper attribution of authorship and correct citation details are given, and that the bibliographic details are not changed. If the article/book is reproduced or disseminated in part, this must be clearly and unequivocally indicated.

Open access has gained tremendous support from both authors, who appreciate the increased visibility of their work, as well as science institutions and funders, who value the societal impact of freely available research results.

SpringerLink에서 검색어로 Deep Learning을 입력하고 2019년부터 2020년사이에 발간된 자료를 찾아보면 아래와 같습니다.

이중 “Trends everywhere? The case of hedge fund styles”을 선택하면 유료로 나옵니다. 구글에서 검색하면 Trends everywhere? The case of hedge fund styles Trends everywhere? The case of hedge fund styles에서 원문을 구할 수 있습니다. 직접 원자료를 제공하지 않더라도 필요한 자료를 접근할 수 있는 방법을 제공합니다.

2.
아래 소개하는 자료의 출처는 Springer has released 65 Machine Learning and Data books for free Springer has released 65 Machine Learning and Data books for free입니다. SpingerLink에서 원문으로 구할 수 있는 기계학습 및 데이타과학과 관련한 책들입니다. 개인적으로 David Ruppert, David S. Matteson가 쓴 Statistics and Data Analysis for Financial Engineering에 관심이 갑니다.

Introduction to Evolutionary Computing A.E. Eiben, J.E. Smith
Analysis for Computer Scientists Michael Oberguggenberger, Alexander Ostermann
Foundations of Programming Languages Kent D. Lee
Concise Guide to Databases Peter Lake, Paul Crowther

Modelling Computing Systems Faron Moller, Georg Struth
Search Methodologies Edmund K. Burke, Graham Kendall
Object-Oriented Analysis, Design and Implementation Brahma Dathan, Sarnath Ramnath

The Elements of Statistical Learning Trevor Hastie, Robert Tibshirani, Jerome Friedman
Introduction to Statistics and Data Analysis Christian Heumann, Michael Schomaker, Shalabh
Data Analysis Siegmund Brandt
Principles of Data Mining Max Bramer
Data Mining Charu C. Aggarwal

Statistics and Analysis of Scientific Data Massimiliano Bonamente
Understanding Analysis Stephen Abbott

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

Introduction to Artificial Intelligence Wolfgang Ertel
Introduction to Deep Learning Sandro Skansi
Neural Networks and Deep Learning Charu C. Aggarwal
Data Science and Predictive Analytics Ivo D. Dinov

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

Linear and Nonlinear Programming David G. Luenberger, Yinyu Ye
Fundamentals of Robotic Mechanical Systems Jorge Angeles

A Beginners Guide to Python 3 Programming John Hunt
Advanced Guide to Python 3 Programming John Hunt
Data Structures and Algorithms with Python Kent D. Lee, Steve Hubbard
The Python Workbook Ben Stephenson

Python For ArcGIS Laura Tateosian

Bayesian Essentials with R Jean-Michel Marin, Christian P. Robert
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
Understanding Statistics Using R Randall Schumacker, Sara Tomek