Open Access Books by Topics

A curated list of List of free/open access libraries and books.

Contributing

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Table of Contents

• [Books]

Computer Vision

• Prince, S. J. (2012). Computer vision: models, learning, and inference. Cambridge University Press.

• Computer Vision: Algorithms and Applications.

Data Science

• Elements of Data Science. Elements of Data Science is an introduction to data science for people with no programming experience. My goal is to present a small, powerful subset of Python that allows you to do real work with data as quickly as possible.

• Wilke, C. O. (2019). Fundamentals of data visualization: a primer on making informative and compelling figures. O’Reilly Media.. The book is intended as a guide to creating visualizations that accurately reflect data, tell a story, and look professional.

• Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for data science. “ O’Reilly Media, Inc.”.. R4DS teaches you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it.

• Peng, R. D., & Matsui, E. (2015). The art of data science. Bookdown.

• Baumer, B. S, Kaplan, D.T, & Horton, N. J. (2023). Modern Data Science with R 2nd edition.

Deep Learning

• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.

• Prince, S. J. (2023). Understanding deep learning. MIT press.

• Fleuret, F. (2023). The little book of deep learning.

• Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. (2023). Dive into deep learning. Cambridge University Press.

• Bishop, C. M., & Bishop, H. (2023). Deep learning: Foundations and concepts. Springer Nature.. It is an online version provider by the author.

• Jentzen, A., Kuckuck, B., & von Wurstemberger, P. (2023). Mathematical introduction to deep learning: methods, implementations, and theory. arXiv preprint arXiv:2310.20360.

Maths

• Interactive Linear AlGebra.

• Gallier J., Quaintance J. (2024). Algebra, Topology, Differential Calculus, and Optimization Theory for Computer Science and Machine Learning..

Machine learning, Statistical Learning

• Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020). Mathematics for machine learning. Cambridge University Press.

• Winn, J. (2023). Model-based machine learning. CRC Press..

• R for Statistical Learning.

• James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An introduction to statistical learning: With applications in python. Springer Nature..

• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: With applications in R Second Edition. Springer Nature..

• Nielsen, M. A. (2015). Neural networks and deep learning (Vol. 25, pp. 15-24). San Francisco, CA, USA: Determination press.

Operating Systems

• Operating Systems: From 0 to 1.

• OsDev.Org.

• Operating Systems: Three Easy Pieces.

• Linux insides.

• How to create and Operating System.

• Corbet, J., Rubini, A., & Kroah-Hartman, G. (2005). Linux device drivers. “ O’Reilly Media, Inc.”..

• Kroah-Hartman, G. (2006). Linux Kernel in a Nutshell: A Desktop Quick Reference. “ O’Reilly Media, Inc.”..

• Downey, A. B., & Press, G. T. (2015). Think os: a brief introduction to operating systems. Allen B. Downey..

• Helin, E., & Renberg, A. The little book about OS development..

• Matthews, S. J., Newhall, T., & Webb, K. C. (2022). Dive Into Systems: A Gentle Introduction to Computer Systems. No Starch Press..

• Shotts, W. The Linux Command Line. Designed for the new command line user, this 594-page volume covers the same material as LinuxCommand.org but in much greater detail. In addition to the basics of command line use and shell scripting, The Linux Command Line includes chapters on many common programs used on the command line, as well as more advanced topics.

Robotics

• Lynch, K. M., & Park, F. C. (2017). Modern robotics. Cambridge University Press.

• Cangelosi, A., & Asada, M. (2022). Cognitive Robotics. The MIT Press.

Statistics and probability

• Cetinkaya-Rundel, M., Hardin, J.(2024). Introduction to Modern Statistics (2e).

• Chan, S. H. (2021). Introduction to probability for data science..

• Davis, C. S. (2002). Statistical methods for the analysis of repeated measurements (p. 174). New York: Springer..

• Diez, D., Cetinkaya-Rundel, M., Barr, C. (2019). OpenIntro Statistics Fourth Edition.

• Faraway, J. J. (2016). Linear models with R. Chapman and Hall/CRC..

• Fein, E. C., Gilmour, J., Machin, T., & Hendry, L. (2022). Statistics for research students: An open access resource with self-tests and illustrative examples. University of Southern Queensland..

• Herzog, M. H., Francis, G., & Clarke, A. (2019). Understanding statistics and experimental design: how to not lie with statistics (p. 142). Springer Nature.. This open access textbook provides the background needed to correctly use, interpret and understand statistics and statistical data in diverse settings.

• Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts..

• James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An introduction to statistical learning: With applications in python. Springer Nature..

• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: With applications in R Second Edition. Springer Nature..

• Lakens, D. (2016). Improving your statistical inferences.

• Moraga, P. (2023). Spatial statistics for data science: theory and practice with R. CRC Press..

• Montesinos López, O. A., Montesinos López, A., & Crossa, J. (2022). Multivariate statistical machine learning methods for genomic prediction (p. 691). Springer Nature..

• Navarro, D. (2015). Learning Statistics with R.

• Díaz-Monroy, L. (2012). Análisis estadístico de datos multivariados. La intención al escribir este texto, es ofrecer un material actualizado de análisis y métodos estadísticos multivariados, de fácil acceso para estadísticos y usuarios de la estadística de diferentes disciplinas y áreas del conocimiento.

Writing, Scientific Writing

• Byfield, B. Designing ebooks with free software. By contrast, Designing ebooks teaches several methods that will allow users to gain control over the creation of their ebooks. Beginners can take control using the desktop, while experts can format the raw code to get the greatest level of precision. All it takes is two tools that are free for the download: LibreOffice and Calibre, plus some trial and error to get the precision and professionalism you want.

• Byfield, B. Designing with LibreOffice. Designing with LibreOffice explains the importance of using styles and templates in order to use LibreOffice with the most convenience and the least effort. By taking advantage of styles and templates, you can concentrate on self-expression, rather than format.

• Ewing, R., & GRUWELL, C. (2023). Critical Thinking in Academic Research - Second Edition.

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