Awesome-Book-paper-tool

A curated list of amazing books, libraries, software, resources and anything.

Programming.

• Python
  https://github.com/CIS3715-temple-2019/CIS3715-temple-2019.github.io/blob/master/CIS3715%20(Temple%20-%20Spring%202019)%20PythonBasics%20-%20Lab1.ipynb

Mathematic

• Calculus
  Chain rule (https://en.wikipedia.org/wiki/Chain_rule)
  partial derivative and gradient (https://en.wikipedia.org/wiki/Partial_derivative
• Matrix Algebra
  https://en.wikipedia.org/wiki/Matrix_(mathematics)
  http://cs229.stanford.edu/section/cs229-linalg.pdf
• Probability Theory
  http://cs229.stanford.edu/section/cs229-prob.pdf

Machine Learning

• S. Shalev-Shwartz, S. Ben-David. Understanding Machine Learning: From Theory to Algorithms, 2014. http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf
• Andreas C. Müller, Sarah Guido. Introduction to Machine Learning with Python, 2017. (https://github.com/amueller/introduction_to_ml_with_python
• K.P. Murphy. Machine Learning: a Probabilistic Perspective, 2012. http://www.cs.ubc.ca/~murphyk/MLbook/

Deep Learning

• Deep Learning textbook https://www.deeplearningbook.org/
• Deep Learning lecture https://www.deeplearningbook.org/lecture_slides.html

Data Mining

• https://www-users.cse.umn.edu/~kumar001/dmbook/index.php#item4Links
• http://myweb.sabanciuniv.edu/rdehkharghani/files/2016/02/The-Morgan-Kaufmann-Series-in-Data-Management-Systems-Jiawei-Han-Micheline-Kamber-Jian-Pei-Data-Mining.-Concepts-and-Techniques-3rd-Edition-Morgan-Kaufmann-2011.pdf