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Barrage Relay Networks

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[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

Data Embedding

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https://www.ibm.com/think/topics/embedding https://www.cloudflare.com/en-gb/learning/ai/what-are-embeddings

IBM Quantum Platorm – Upgraded

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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

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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

Differential Privacy

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Heads and Tails Analogue Laplace Distribution Apple, Google using DP. https://privacytools.seas.harvard.edu/differential-privacy https://www.labelia.org/en/blog/the-nuts-and-bolts-of-differential-privacy-part1https://towardsdatascience.com/a-differential-privacy-example-for-beginners-ef3c23f69401/ References:

Latex Tips

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$pdflatex = “latexdiff –graphics-markup=0 backup.tex main.tex > main-d.tex; pdflatex %O main-d”
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