# taylor series

Published articles for taylor series.

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## Taylor Series and Accelerometers

DevFeed: [Taylor Series and Accelerometers](<https://devfeed.tech/articles/taylor-series-and-accelerometers-40436.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2020/07/26/taylor-series-and-accelerometers/>)

Published: 2020-07-26T11:11:41Z

Content type: tutorial

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [Mathematics](<https://devfeed.tech/topics/mathematics.md>), [circuit](<https://devfeed.tech/topics/circuit.md>)

Tags: [accelerometers](<https://devfeed.tech/tags/accelerometers.md>), [capacitors](<https://devfeed.tech/tags/capacitors.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [physics](<https://devfeed.tech/tags/physics.md>), [smartphone](<https://devfeed.tech/tags/smartphone.md>), [taylor-series](<https://devfeed.tech/tags/taylor-series.md>)

### AI overview

An explanation of how Taylor series relate to the design of modern smartphone accelerometers, including the capacitor principles used in these devices.

### Source excerpt

In my book, A Programmer's Introduction to Mathematics, I describe the Taylor Series as a "hammer for every nail." I learned about another nail in the design of modern smartphone accelerometers from "Eight Amazing Engineering Stories" by Hammack, Ryan, and Ziech, which I'll share here. These accelerometers are designed using a system involving three plates, which correspond to two capacitors. A quick recap on my (limited) understanding of how capacitors work.

## The Inequality

DevFeed: [The Inequality](<https://devfeed.tech/articles/the-inequality-40392.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2015/11/23/the-inequality/>)

Published: 2015-11-23T22:06:27Z

Content type: tutorial

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [math](<https://devfeed.tech/topics/math.md>), [Computer science](<https://devfeed.tech/topics/computer-science.md>), [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>)

Tags: [calculus](<https://devfeed.tech/tags/calculus.md>), [cauchy-schwarz](<https://devfeed.tech/tags/cauchy-schwarz.md>), [chernoff-bound](<https://devfeed.tech/tags/chernoff-bound.md>), [computer-science](<https://devfeed.tech/tags/computer-science.md>), [euler-s-number](<https://devfeed.tech/tags/euler-s-number.md>), [inequality](<https://devfeed.tech/tags/inequality.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [math](<https://devfeed.tech/tags/math.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [taylor-series](<https://devfeed.tech/tags/taylor-series.md>)

### AI overview

This tutorial explains how the inequality 1+x ≤ e^x is used to bound the probability of bad events in machine-learning algorithm analysis and introduces its use in proving the weighted arithmetic-geometric mean inequality.

### Source excerpt

Math and computer science are full of inequalities, but there is one that shows up more often in my work than any other. Of course, I'm talking about $$\displaystyle 1+x \leq e^{x}$$ This is The Inequality. I've been told on many occasions that the entire field of machine learning reduces to The Inequality combined with the Chernoff bound (which is proved using The Inequality). Why does it show up so often in machine learning?