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Calculus – Transcendental Functions

by ComputingNotes
References: Transcendental Functions In mathematics, a transcendental function is a function that does not satisfy a polynomial equation with polynomial coefficients. To put it simply: it is any function that is not algebraic. While algebraic functions can be constructed using a finite number of elementary operations (addition, subtraction, multiplication, division, and taking roots), transcendental functions ... Read More

Calculus – Initial Value Problems

by ComputingNotes
Book Calculus by Thomas/Finney, 9th Edition, Page 282 Understanding Initial Value Problems in Calculus When studying differential equations in calculus, we often encounter something called an Initial Value Problem (IVP). While differential equations describe entire families of functions, an initial value problem helps us find one specific solution that fits a given condition. What Is ... Read More

Graph

by ComputingNotes
https://en.wikipedia.org/wiki/Graph_(discrete_mathematics) https://algs4.cs.princeton.edu/41graph https://au.mathworks.com/help/matlab/math/directed-and-undirected-graphs.html https://mathinsight.org/definition/undirected_graph https://medium.com/basecs/a-gentle-introduction-to-graph-theory-77969829ead8 https://www.jsums.edu/nmeghanathan/files/2015/05/CSC434-Fall2014-Module-1-Graph-Theory-Basics.pdf

Barrage Relay Networks

by ComputingNotes
[1] T. R. Halford and K. M. Chugg, “Barrage Relay Networks,” in 2010 Information Theory and Applications Workshop (ITA), Jan. 2010, pp. 1–8. doi: 10.1109/ITA.2010.5454129. [2] T. R. Halford, K. M. Chugg, and A. Polydoros, “Barrage relay networks: System & protocol design,” in 21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, ... Read More

IBM Quantum Platorm – Upgraded

by ComputingNotes
https://www.ibm.com/quantum/blog/iqp-upgrade https://quantum.cloud.ibm.com/docs/en/migration-guides/classic-iqp-to-cloud-iqp New version of IBM Quantum Platformhttps://quantum.cloud.ibm.com/ https://quantum.cloud.ibm.com/docs/en/guides/cloud-setup Major Differences:https://quantum.cloud.ibm.com/docs/en/migration-guides/classic-iqp-to-cloud-iqp#major-differences

Gradient Descent

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Iterative process: Goal of gradient descent: MINIMIZE the cost function (error between predicted and actual value) Requirements: a Learning Rate and the direction where to go. Terms: Types of Gradient Descents: https://www.ibm.com/think/topics/gradient-descent Linear Regression: Gradient Descent (One of the best source)https://developers.google.com/machine-learning/crash-course/linear-regression/gradient-descentMath behind gradient descent: https://sebastianraschka.com/faq/docs/gradient-optimization.html https://www.khanacademy.org/math/multivariable-calculus/applications-of-multivariable-derivatives/optimizing-multivariable-functions/a/what-is-gradient-descent https://www.geeksforgeeks.org/gradient-descent-algorithm-and-its-variants https://en.wikipedia.org/wiki/Gradient_descent

Privacy in ML

by ComputingNotes
What is DP? What is privacy in first place? What do we refer to privacy in ML? What is the main difference between: PCA and PCA DP? Same with SGD vs SGD DP? Why do we need DP? What are the problems in Machine Learning in terms of privacy? How to quantify privacy? Loss of ... Read More

VENV virtual environment

by ComputingNotes
python -m venv venv Windows PS: .\venv\Scripts\Activate.ps1Mac OS: source venv/bin/activateLinus: https://docs.python.org/3/library/venv.html
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