A list of references to blog posts, articles, books etc. I find nice.

Blog Posts

• A Breif Introduction to Time Series Classification Algorithms: https://towardsdatascience.com/a-brief-introduction-to-time-series-classification-algorithms-7b4284d31b97

Articles

• Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases: https://www.cs.ucr.edu/~eamonn/kais_2000.pdf
• Attention Is All You Need: https://arxiv.org/abs/1706.03762

Books

• Cython a Guide for Python Programmers: http://www.jyguagua.com/wp-content/uploads/2017/03/OReilly.Cython-A-Guide-for-Python-Programmers.pdf
• Cython a Guide for Python Programmers - Examples: https://github.com/cythonbook/examples. Book Author: https://github.com/kwmsmith.
• An Introduction to Statistical Learning: https://www.statlearning.com
• The Elements of Statistical Learning: https://hastie.su.domains/Papers/ESLII.pdf
• Foundations of Machine Learning: https://cs.nyu.edu/~mohri/mlbook/
• Boosting Foundations and Algorithms: https://doc.lagout.org/science/0_Computer%20Science/2_Algorithms/Boosting_%20Foundations%20and%20Algorithms%20%5BSchapire%20%26%20Freund%202012-05-18%5D.pdf
• R for Data Science: https://r4ds.had.co.nz
• Learning Spark: https://pages.databricks.com/rs/094-YMS-629/images/LearningSpark2.0.pdf
• The Unix Workbench: https://seankross.com/the-unix-workbench/
• Pattern Recognition and Machine Learning: https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf

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