Intermediate Algebra-Inverse Functions
Intermediate Algebra-Inverse Functions
From playlist Intermediate Algebra
Julia Komjathy: Weighted distances in scale free random graph models
Abstract: In this talk I will review the recent developments on weighted distances in scale free random graphs as well as highlight key techniques used in the proofs. We consider graph models where the degree distribution follows a power-law such that the empirical variance of the degrees
From playlist Probability and Statistics
Sahand Neghaban: Individualized rank aggregation using nuclear norm regularization
Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b
From playlist Probability and Statistics
Math 101 090817 Introduction to Analysis 04 Ordered fields
Ordered sets. Examples. Ordered fields. Properties of ordered fields.
From playlist Course 6: Introduction to Analysis (Fall 2017)
Network Analysis. Lecture 9. Graph partitioning algorithms
Graph density. Graph pertitioning. Min cut, ratio cut, normalized and quotient cuts metrics. Spectral graph partitioning (normalized cut). Direct (spectral) modularity maximization. Multilevel recursive partitioning Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/le
From playlist Structural Analysis and Visualization of Networks.
Normalizer of a set in a group
The normalizer of a set in a group is the bigger cousin of the centralizer. In fact, the centralizer is a subset of the normalizer. We relax the conditions a bit and let the conjugation of an element result in any arbitrary element in the subset. Not making any sense? Just watch the vi
From playlist Abstract algebra
Excel Statistical Analysis 10: Weighted Mean. Awesome Accounting Example!!
Download Excel File: https://excelisfun.net/files/Ch03-ESA.xlsm Learn about how to calculate the weighted mean, or weighted average, a calculation done often in business and accounting. Learn all about the SUMPRODUCT function. Topics: 1. (00:00) Introduction 2. (00:40) Weighted Mean Formul
From playlist Excel Statistical Analysis for Business Class Playlist of Videos from excelisfun
Reshape, Subset, and Summarize Data | Introduction to dplyr Part 2
We cover some basic functions of dplyr including the mighty group_by and summarize combo that makes dividing up datasets a breeze, as well as arrange, select, and filter that help get the data in a cleaner and more organized format. Group-by aggregation is one of the most powerful, yet sim
From playlist Introduction to dplyr
CS224W: Machine Learning with Graphs | 2021 | Lecture 7.2 - A Single Layer of a GNN
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Bu5VmV Jure Leskovec Computer Science, PhD Under the general perspective on GNN, we first introduce the concept of a general GNN layer. A general GNN layer consis
From playlist Stanford CS224W: Machine Learning with Graphs
TAPAS: Weakly Supervised Table Parsing via Pre-training (Paper Explained)
Answering complex questions about tabular information is hard. No two tables are alike and sometimes the answer you're looking for is not even in the table and needs to be computed from a subset of the cells. Surprisingly, this model can figure it all out by itself through some clever inpu
From playlist Papers Explained
Gilles Stoltz: Robust sequential learning with applications to the forecasting [...]
Abstract: Sometimes, you feel you're spoilt for choice: there are so many good predictors that you could use! Why select and focus on just one? I will review the framework of robust online aggregation (also known as prediction of individual sequences or online aggregation of expert advice)
From playlist Probability and Statistics
TensorFlow Federated Tutorial Session
A Google TechTalk, 2020/7/31, presented by Google Research Staff ABSTRACT:
From playlist 2020 Google Workshop on Federated Learning and Analytics
AMMI 2022 Course "Geometric Deep Learning" - Lecture 5 (Graphs & Sets) - Petar Veličković
Video recording of the course "Geometric Deep Learning" taught in the African Master in Machine Intelligence in July 2022 by Michael Bronstein (Oxford), Joan Bruna (NYU), Taco Cohen (Qualcomm), and Petar Veličković (DeepMind) Lecture 5: Learning on sets • Permutations • Permutation invari
From playlist AMMI Geometric Deep Learning Course - Second Edition (2022)
Excel 2013 Statistical Analysis #28: Multiplication Law of Probability AND Events (16 Examples)
Download Excel file: https://people.highline.edu/mgirvin/AllClasses/210Excel2013/Ch04/Excel2013StatisticsChapter04.xlsm Download pdf notes file: https://people.highline.edu/mgirvin/AllClasses/210Excel2013/Ch04/Ch04PDFBusn210.pdf Topics in this video: 1. (00:25) Conditional Probability (3 E
From playlist Excel for Statistical Analysis in Business & Economics Free Course at YouTube (75 Videos)
[GAT] Graph Attention Networks | AISC Foundational
For more details including paper and slides, visit https://aisc.a-i.science/events/2019-04-15/
From playlist Graph Neural Networks
[GAT] Graph Attention Networks | AISC Foundational
For more details including paper and slides, visit https://aisc.a-i.science/events/2019-04-15/
From playlist Graph Neural Networks
Lecture 9: Risk-Sharing with Production
MIT 14.04 Intermediate Microeconomic Theory, Fall 2020 Instructor: Prof. Robert Townsend View the complete course: https://ocw.mit.edu/courses/14-04-intermediate-microeconomic-theory-fall-2020/ YouTube Playlist: https://www.youtube.com/watch?v=XSTSfCs74bg&list=PLUl4u3cNGP63wnrKge9vllow3Y2
From playlist MIT 14.04 Intermediate Microeconomic Theory, Fall 2020
Lec 9 | MIT 2.830J Control of Manufacturing Processes, S08
Lecture 9: Advanced and multivariate SPC Instructor: Duane Boning, David Hardt View the complete course at: http://ocw.mit.edu/2-830JS08 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 2.830J, Control of Manufacturing Processes S08
Stéphane Mallat - Multiscale Models for Image Classification and Physics with Deep Networks
Abstract: Approximating high-dimensional functionals with low-dimensional models is a central issue of machine learning, image processing, physics and mathematics. Deep convolutional networks are able to approximate such functionals over a wide range of applications. This talk shows that t
From playlist 2nd workshop Nokia-IHES / AI: what's next?