Approximate computing is an emerging paradigm for energy-efficient and/or high-performance design. It includes a plethora of computation techniques that return a possibly inaccurate result rather than a guaranteed accurate result, and that can be used for applications where an approximate result is sufficient for its purpose. One example of such situation is for a search engine where no exact answer may exist for a certain search query and hence, many answers may be acceptable. Similarly, occasional dropping of some frames in a video application can go undetected due to perceptual limitations of humans. Approximate computing is based on the observation that in many scenarios, although performing exact computation requires large amount of resources, allowing bounded approximation can provide disproportionate gains in performance and energy, while still achieving acceptable result accuracy. For example, in k-means clustering algorithm, allowing only 5% loss in classification accuracy can provide 50 times energy saving compared to the fully accurate classification. The key requirement in approximate computing is that approximation can be introduced only in non-critical data, since approximating critical data (e.g., control operations) can lead to disastrous consequences, such as program crash or erroneous output. (Wikipedia).
Polynomial approximation of functions (part 1)
Using a polynomial to approximate a function at f(0). More free lessons at: http://www.khanacademy.org/video?v=sy132cgqaiU
From playlist Calculus
Using Taylor Polynomials to Approximate Functions
This video shows how to determine a Taylor Polynomial to approximate a function. http://mathispower4u.yolasite.com/
From playlist Infinite Sequences and Series
(New Version Available) Inverse Functions
New Version: https://youtu.be/q6y0ToEhT1E Define an inverse function. Determine if a function as an inverse function. Determine inverse functions. http://mathispower4u.wordpress.com/
From playlist Exponential and Logarithmic Expressions and Equations
Approximation & Estimation | Numbers | Maths | FuseSchool
An approximation is anything that is similar, but not exactly the same as something else. For example, if you were to say a 57 minute journey would take “about an hour”, you would be approximating. A value can be approximated by rounding, usually to a value that it is easier to work with
From playlist MATHS: Numbers
Numerical data explained | Introducing digits, range and precision for programming beginners
Numerical data is one of the two types of data inside every computer program, and we use numerical data to represent the outside world. Let's take a detailed look at numerical data inside computer programs. 1) Numerical data inside computer programs: Integers and decimals 2) How numbers r
From playlist Data Science - Learn to code for beginners
Ex 1: Find the Inverse of a Function
This video provides two examples of how to determine the inverse function of a one-to-one function. A graph is used to verify the inverse function was found correctly. Library: http://mathispower4u.com Search: http://mathispower4u.wordpress.com
From playlist Determining Inverse Functions
Visualizing decimal numbers and their arithmetic 67 | Arithmetic and Geometry Math Foundations
This video gives a precise definition of a decimal number as a special kind of rational number; one for which there is an expression a/b where a and b are integers, with b a power of ten. For such a number we can extend the Hindu-Arabic notation for integers by introducing the decimal form
From playlist Math Foundations
Computing Statistics - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Measurement, approximation and interval arithmetic (I) | Real numbers and limits Math Foundations 81
This video introduces interval arithmetic, first in the context of natural numbers, and then for integers. We start with some remarks from the previous video on the difficulties with irrational numbers, sqrt(2), pi and e. Then we give some general results about order (less than, greater
From playlist Math Foundations
Madeleine Udell: "Low Rank Tucker Approximation of a Tensor from Streaming Data"
Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021 Workshop III: Mathematical Foundations and Algorithms for Tensor Computations "Low Rank Tucker Approximation of a Tensor from Streaming Data" Madeleine Udell - Cornell University, Computational and Mathematica
From playlist Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021
Linear approximations may be good enough -- Calculus I
This lecture is on Calculus I. It follows Part I of the book Calculus Illustrated by Peter Saveliev. The text of the book can be found at http://calculus123.com.
From playlist Calculus I
Slides and more information: https://mml-book.github.io/slopes-expectations.html
From playlist There and Back Again: A Tale of Slopes and Expectations (NeurIPS-2020 Tutorial)
From playlist COMP0168 (2020/21)
From playlist CS294-112 Deep Reinforcement Learning Sp17
“Choice Modeling and Assortment Optimization” – Session III – Prof. Huseyin Topaloglu
This module overviews static and dynamic assortment optimization problems. We will start with an introduction to discrete choice modeling and discuss estimation issues when fitting a choice model to observed sales histories. Following this introduction, we will discuss static and dynamic a
From playlist Thematic Program on Stochastic Modeling: A Focus on Pricing & Revenue Management
MegaFavNumbers | The magic number and the legendary fast inverse square root hack.
Hi! I'm Rodrigo Aldana. This is my contribution to the #MegaFavNumbers project. This video is based on a presentation I gave some time ago about the fast inverse square root algorithm but now focused on the related magic number 1597463007. I want to make something clear: 1597463007 is not
From playlist MegaFavNumbers
SketchySVD - Joel Tropp, California Institute of Technology
This workshop - organised under the auspices of the Isaac Newton Institute on “Approximation, sampling and compression in data science” — brings together leading researchers in the general fields of mathematics, statistics, computer science and engineering. About the event The workshop ai
From playlist Mathematics of data: Structured representations for sensing, approximation and learning
Newton's method for finding zeroes | Real numbers and limits Math Foundations 83 | N J Wildberger
Newton, the towering scientific figure of the 17th century, discovered a lovely method for finding approximate solutions to equations, involving iterated constructions of tangent lines and their intersections. We describe this method in general and then apply it to the simplest and most fa
From playlist Math Foundations
Debabrota Basu (6/17/20): Epsilon-net induced lazy witness complex for topological data analysis
Title: Epsilon-net induced lazy witness complex for efficient topological data analysis Abstract: Inefficient scalability of persistent homology computation on simplicial representations restrains practical application of TDA. The lazy witness complex economically defines an approximate r
From playlist AATRN 2020