X-parameters are a generalization of S-parameters and are used for characterizing the amplitudes and relative phase of harmonics generated by nonlinear components under large input power levels. X-parameters are also referred to as the parameters of the Poly-Harmonic Distortion (PHD) nonlinear behavioral model. (Wikipedia).
Label x and y intercepts from a graph
👉 Learn about the characteristics of a function. Given a function, we can determine the characteristics of the function's graph. We can determine the end behavior of the graph of the function (rises or falls left and rises or falls right). We can determine the number of zeros of the functi
From playlist Characteristics of Functions
What does the parameter mean? (1 of 2: Exploring the derivatives)
More resources available at www.misterwootube.com
From playlist Further Work with Functions
Determine if an Equation Represents a Function (Basic with Definition Only)
This video explains how to determine if a given equation represents a function using the definition of a function. http://mathispower4u.com
From playlist Determining if a Relations is a Function
Statistics / Data Analysis (Lecture 1) by B. Wandelt
Program Cosmology - The Next Decade ORGANIZERS : Rishi Khatri, Subha Majumdar and Aseem Paranjape DATE : 03 January 2019 to 25 January 2019 VENUE : Ramanujan Lecture Hall, ICTS Bangalore The great observational progress in cosmology has revealed some very intriguing puzzles, the most i
From playlist Cosmology - The Next Decade
Gradients in Machine Learning with Jon Krohn
The gradient captures the partial derivative of cost with respect to all of our machine learning model's parameters. To come to grips with it, Jon Krohn carries out a regression on individual data points and derives the partial derivatives of quadratic cost. He then gets into what it means
From playlist Talks and Tutorials
Generalized Linear Model (Part A)
Regression Analysis by Dr. Soumen Maity,Department of Mathematics,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in
From playlist IIT Kharagpur: Regression Analysis | CosmoLearning.org Mathematics
04-2 Sensitivity Analysis Global
Sobol' and regionalized sensitivity analysis
From playlist QUSS GS 260
From playlist COMP0168 (2020/21)
Quantum Entanglement, Bell Inequality, EPR paradox
Quantum Entanglement, EPR paradox, Bell Inequality, and the implication for Einstein's Theory of Relativity.
From playlist Physics
Machine learning - Gaussian processes
Regression with Gaussian processes Slides available at: http://www.cs.ubc.ca/~nando/540-2013/lectures.html Course taught in 2013 at UBC by Nando de Freitas
From playlist Machine Learning 2013
Michael Lesnick (2/23/2022): Stability of 2-Parameter Persistent Homology
We show that the standard stability results for union-of-balls, ÄŒech, and Rips persistent homology have natural analogues in the 2-parameter setting, formulated in terms of the multicover bifiltration and Sheehy's subdivision bifiltrations. Our results imply that these bifiltrations are r
From playlist AATRN 2022
Singular Learning Theory - Seminar 1 - What is learning?
This seminar series is an introduction to Watanabe's Singular Learning Theory, a theory about algebraic geometry and statistical learning theory. In this first lecture Dan Murfet gives a high level overview of what learning is, and why degeneracies or singularities are naturally encountere
From playlist Metauni
DeepMind x UCL | Deep Learning Lectures | 11/12 | Modern Latent Variable Models
This lecture, by DeepMind Research Scientist Andriy Mnih, explores latent variable models, a powerful and flexible framework for generative modelling. After introducing this framework along with the concept of inference, which is central to it, Andriy focuses on two types of modern latent
From playlist Learning resources
