In linguistics, especially within generative grammar, phi features (denoted with the Greek letter φ 'phi') are the morphological expression of a semantic process in which a word or morpheme varies with the form of another word or phrase in the same sentence. This variation can include person, number, gender, and case, as encoded in pronominal agreement with nouns and pronouns (the latter are said to consist only of phi-features, containing no lexical head). Several other features are included in the set of phi-features, such as the categorical features ±N (nominal) and ±V (verbal), which can be used to describe lexical categories and case features. Phi-features are often thought of as the "silent" features that exist on lexical heads (or, according to some theories, within the syntactic structure) that are understood for number, gender, person or reflexivity. Due to their silent nature, phi-features are often only understood if someone is a native speaker of a language, or if the translation includes a gloss of all these features. Many languages exhibit a pro-drop phenomenon which means that they rely on other lexical categories to determine the phi-features of the lexical heads. (Wikipedia).
Number Theory: Euler's Phi Function / Euler's Theorem
An introduction to Euler's Phi Function and Euler's Theorem
From playlist Basics: Number Theory
From general etale (phi, Gamma)-modules to representations of G(Q_p) - Marie-France Vigneras
Marie-France Vigneras Institut de Mathematiques de Jussieu March 24, 2011 For more videos, visit http://video.ias.edu
From playlist Mathematics
Sample problems on vector fields, gradient, divergence and curl, and line integrals.
From playlist MATH2018 Engineering Mathematics 2D
Physics - Ch 66.5 Quantum Mechanics: The Hydrogen Atom (21 of 78) Schrodinger Eqn. PHI=?
Visit http://ilectureonline.com for more math and science lectures! In this video I will explain the 1st differential equation of the function PHI as a function of phi, PHI(phi), and the solution to that function that describe the motion of the position of the electron relative to the x,y
From playlist PHYSICS 66.5 QUANTUM MECHANICS: THE HYDROGEN ATOM
Differential Equations | Exact Equations
We derive the method for solving exact differential equations and give an example.
From playlist Exact Differential Equations
PhiTOP | Creative Gift Ideas | Exploratorium
Shop for the PhiTOP and explore other gift ideas at exploratoriumstore.com The PhiTOP is a 21st-century dynamical object with a unique mathematical shape. It is in the broadest sense a “philosophical toy.” It can be spun like a top – which it is. Or it can sit quietly on a desk to be enjo
From playlist Creative Gift Ideas
Computing cyclotomic polynumbers | Famous Math Problems 20 | N J Wildberger
The cyclotomic polynomials -- or polynumbers as we prefer to call them --- (called Phi_n) are an important family arising from a simple factorization problem, but having major applications to algebra, number theory and indeed also to geometry. Initially they arise from the problem of facto
From playlist Famous Math Problems
Potential function example (gradient)
Free ebook http://tinyurl.com/EngMathYT Simple example involving the calculation of a potential function. The ideas find applications in vector calculus and line integrals.
From playlist Several Variable Calculus / Vector Calculus
Lecture 7 - Kernels | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GftN16 Andrew Ng Adjunct Professor of Computer Science https://www.andrewng.org/ To follow along with the course schedule and syllabus, visit: http://cs229.sta
From playlist Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018
CS224W: Machine Learning with Graphs | 2021 | Lecture 5.2 - Relational and Iterative Classification
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GiEnnU Jure Leskovec Computer Science, PhD In this video we introduce the relational classifier and iterative classification for node classification. Starting fro
From playlist Stanford CS224W: Machine Learning with Graphs
Stanford CS229: Machine Learning | Summer 2019 | Lecture 8 - Kernel Methods & Support Vector Machine
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3DYVYzo Anand Avati Computer Science, PhD To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-summer2019.html
From playlist Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati)
The Kernel Trick - THE MATH YOU SHOULD KNOW!
Some parametric methods, like polynomial regression and Support Vector Machines stand out as being very versatile. This is due to a concept called "Kernelization". In this video, we are going to kernelize linear regression. And show how they can be incorporated in other Algorithms to solv
From playlist The Math You Should Know
Physics Ch 67.1 Advanced E&M: Review Vectors (79 of 113) What is x-hat in Terms of r, theta, phi?
Visit http://ilectureonline.com for more math and science lectures! To donate: http://www.ilectureonline.com/donate https://www.patreon.com/user?u=3236071 We will find x-hat=? in terms of spherical coordinates of r, theta, phi. Next video in this series can be seen at: https://youtu.be/
From playlist PHYSICS 67.1 ADVANCED E&M VECTORS & FIELDS
Lecture 8 | Machine Learning (Stanford)
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng continues his lecture about support vector machines, including soft margin optimization and kernels. This course provides a broad introduction to machine learning and
From playlist Lecture Collection | Machine Learning
CS224W: Machine Learning with Graphs | 2021 | Lecture 9.2 - Designing the Most Powerful GNNs
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3nGksXo Jure Leskovec Computer Science, PhD In this lecture, we aim to design a maximally expressive GNN model. Our key insight is that a maximally expressive GNN
From playlist Stanford CS224W: Machine Learning with Graphs
Support Vector Machines - THE MATH YOU SHOULD KNOW
In this video, we are going to see exactly why SVMs are so versatile by getting into the math that powers it. If you like this video and want to see more content on data Science, Machine learning, Deep Learning and AI, hit that SUBSCRIBE button. And ring that damn bell for notifications
From playlist The Math You Should Know
Machine Learning 2 - Features, Neural Networks | Stanford CS221: AI (Autumn 2019)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GrSkjF Topics: Features and non-linearity, Neural networks, nearest neighbors Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford Unive
From playlist Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2019
Stanford EE104: Introduction to Machine Learning | 2020 | Lecture 5 - features
Professor Sanjay Lall Electrical Engineering To follow along with the course schedule and syllabus, visit: http://ee104.stanford.edu To view all online courses and programs offered by Stanford, visit: https://online.stanford.edu/
From playlist Stanford EE104: Introduction to Machine Learning Full Course
The PhiTOP: A Golden Ellipsoid – Kenneth Brecher
A new philosophical toy or demonstration object has been developed with elegant dynamical properties and beautiful aesthetic appeal. This "PhiTOP" is a metal prolate ellipsoid that incorporates the golden section in its shape.
From playlist G4G12 Videos
Lecture 4 | Machine Learning (Stanford)
Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on Newton's method, exponential families, and generalized linear models and how they relate to machine learning. This course provides a broad introduction to
From playlist Lecture Collection | Machine Learning