Computer vision and macine learning background

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▼This page is a literature review.Feel free to edit this page and add sources and summaries.

• https://www.learnopencv.com/
• https://docs.opencv.org/3.2.0/d9/df8/tutorial_root.html

• https://medium.com/machine-learning-in-practice/over-200-of-the-best-machine-learning-nlp-and-python-tutorials-2018-edition-dd8cf53cb7dc
• http://web.archive.org/web/20201204051345/https://www.kaggle.com/kanncaa1/data-sciencetutorial-for-beginners
• http://web.archive.org/web/20201112012411/https://www.kaggle.com/kanncaa1/machine-learning-tutorial-for-beginners

There are also a number of classical books for both topics:

• Richard Szeliski. Computer Vision: Algorithms and Applications. Springer, 2010.

http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf

• Gary Bradski and Adrian Kaehler. Learning OpenCV. O'Reilly, 2008.

https://www.bogotobogo.com/cplusplus/files/OReilly%20Learning%20OpenCV.pdf

• Ian Goodfellow, Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press, 2016.

https://github.com/janishar/mit-deep-learning-book-pdf

• Christopher Bishop. Pattern recognition and machine learning. Springer, 2006.

https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf