Topological graph theory | Graph minor theory | Knot theory
In topological graph theory, a mathematical discipline, a linkless embedding of an undirected graph is an embedding of the graph into three-dimensional Euclidean space in such a way that no two cycles of the graph are linked. A flat embedding is an embedding with the property that every cycle is the boundary of a topological disk whose interior is disjoint from the graph. A linklessly embeddable graph is a graph that has a linkless or flat embedding; these graphs form a three-dimensional analogue of the planar graphs. Complementarily, an intrinsically linked graph is a graph that does not have a linkless embedding. Flat embeddings are automatically linkless, but not vice versa. The complete graph K6, the Petersen graph, and the other five graphs in the Petersen family do not have linkless embeddings. Every graph minor of a linklessly embeddable graph is again linklessly embeddable, as is every graph that can be reached from a linklessly embeddable graph by a Y-Δ transform. The linklessly embeddable graphs have the Petersen family graphs as their forbidden minors, and include the planar graphs and apex graphs. They may be recognized, and a flat embedding may be constructed for them, in O(n2). (Wikipedia).
Graph Neural Networks, Session 6: DeepWalk and Node2Vec
What are Node Embeddings Overview of DeepWalk Overview of Node2vec
From playlist Graph Neural Networks (Hands-on)
Word embeddings are one of the coolest things you can do with Machine Learning right now. Try the web app: https://embeddings.macheads101.com Word2vec paper: https://arxiv.org/abs/1301.3781 GloVe paper: https://nlp.stanford.edu/pubs/glove.pdf GloVe webpage: https://nlp.stanford.edu/proje
From playlist Machine Learning
Adding Connections on LinkedIn
In this video, you’ll learn how to add connections on LinkedIn. Visit https://edu.gcfglobal.org/en/linkedin/adding-connections-on-linkedin/1/ for our text-based lesson. We hope you enjoy!
From playlist LinkedIn
First look at Knowledge Graph Embedding (w/ simple Jupyter NB dgl-ke)
Knowledge Graph Embedding and its advantages for answering search queries. Simple explanation of Knowledge Graph Embedding and its use case. Tech to answer your (Siri) questions is basically a Deep Graph Knowledge Embedding Library (DGL-KE), a knowledge graph (KG) embeddings library built
From playlist Learn Graph Neural Networks: code, examples and theory
In this video, you’ll learn about how links function in HTML. We hope you enjoy! To learn more, check out our Basic HTML tutorial here: https://edu.gcfglobal.org/en/basic-html/ #html #links #coding
From playlist HTML
Rasa Algorithm Whiteboard - Understanding Word Embeddings 1: Just Letters
We're making a few videos that highlight word embeddings. Before training word embeddings we figured it might help the intuition if we first trained some letter embeddings. It might suprise you but the idea with an embedding can also be demonstrated with letters as opposed to words. We're
From playlist Algorithm Whiteboard
WebAssembly: The What, Why and How
WebAssembly is a portable, size, and load-time efficient binary format for the web. It is an emerging standard being developed in the WebAssembly community group, and supported by multiple browser vendors. This talk details what WebAssembly is, the problems it is trying to solve, exciting
From playlist Talks
Linked List in Java | Java Linked Explained | Data Structures Implementation | Edureka
🔥 Java, J2EE & SOA Certification Training - https://www.edureka.co/java-j2ee-training-course This Edureka video will provide you with detailed knowledge about Linked Lists in Java and along with it, This video will also cover some examples of Linked Lists in Java, in order to provide you w
From playlist Java Tutorial For Beginners | Edureka
Jasna Urbančič (11/03/21):Optimizing Embedding using Persistence
Title: Optimizing Embedding using Persistence Abstract: We look to optimize Takens-type embeddings of a time series using persistent (co)homology. Such an embedding carries information about the topology and geometry of the dynamics of the time series. Assuming that the input time series
From playlist AATRN 2021
Data Structures in Java | Stack, Queue, LinkedList, Tree in Data Structures | Edureka
** Java Certification Training: https://www.edureka.co/java-j2ee-training-course ** This Edureka video on “Data Structures in Java” will talk about Stack, Queue, LinkedList, Tree in Data Structures with examples. Following are the topics covered in this video: What is Data Structure? Wha
From playlist Java Tutorial For Beginners | Edureka
Lecture 26 | Programming Abstractions (Stanford)
Lecture 26 by Julie Zelenski for the Programming Abstractions Course (CS106B) in the Stanford Computer Science Department. Julie ties up the "loose ends" of the course: after a general review of the concepts covered in the course, she asks which of two examples is the better. She then
From playlist Lecture Collection | Programming Abstractions
Leetcode Short [C | Vim] - Problem 141: Linked List Cycle
[ SURPRISE: This one is done in C instead of Rust because Leetcode does not supply a Rust template for this problem. They must have had issues with the borrow-checker... ] I'm working my way through the "Grind 75" Leetcode problems, as a sort of warmup to Advent of Code coming in December
From playlist Leetcode
Related videos: Linked list intro: https://youtu.be/-Yn5DU0_-lw Linked list code: https://youtu.be/m-8ZBO2ywaU Data Structures Source Code: https://github.com/williamfiset/algorithms My website: http://www.williamfiset.com
From playlist Data structures playlist
Digital Marketing Course | Digital Marketing Tutorial For Beginners | Digital Marketing |Simplilearn
🔥Digital Marketing Specialist Program (Discount Code - YTBE15): https://www.simplilearn.com/advanced-digital-marketing-certification-training-course?utm_campaign=DigitalMarketing-kpgerCE095A&utm_medium=Descriptionff&utm_source=youtube 🔥Professional Certificate Program In Digital Marketing
From playlist Digital Marketing Playlist [2023 Updated]🔥 | Digital Marketing Course | Digital Marketing Tutorial For Beginners | Simplilearn
Lecture 21 | Programming Abstractions (Stanford)
Lecture 21 by Julie Zelenski for the Programming Abstractions Course (CS106B) in the Stanford Computer Science Department. Julie talks about the buffer version of vector vs. stack and follows this with an example of cursor design. She also talks about linked list insertion and deletion
From playlist Lecture Collection | Programming Abstractions
The Insane Engineering of the A-10 Warthog
Sign up to Nebula here: https://go.nebula.tv/realengineering Patreon: https://www.patreon.com/user?u=2825050&ty=h Facebook: http://facebook.com/realengineering1 Instagram: https://www.instagram.com/brianjamesmcmanus Reddit: https://www.reddit.com/r/RealEngineering/ Twitter: http
From playlist Military
Lecture 4 - Elementary Data Structures
This is Lecture 4 of the CSE373 (Analysis of Algorithms) course taught by Professor Steven Skiena [http://www3.cs.stonybrook.edu/~skiena/] at Stony Brook University in 2016. The lecture slides are available at: https://www.cs.stonybrook.edu/~skiena/373/newlectures/lecture4.pdf More infor
From playlist CSE373 - Analysis of Algorithms 2016 SBU
Anna Wienhard (7/29/22): Graph Embeddings in Symmetric Spaces
Abstract: Learning faithful graph representations has become a fundamental intermediary step in a wide range of machine learning applications. We propose the systematic use of symmetric spaces as embedding targets. We use Finsler metrics integrated in a Riemannian optimization scheme, that
From playlist Applied Geometry for Data Sciences 2022
This is Lecture 5 of the CSE373 (Analysis of Algorithms) course taught by Professor Steven Skiena [http://www3.cs.stonybrook.edu/~skiena/] at Stony Brook University in 2016. The lecture slides are available at: https://www.cs.stonybrook.edu/~skiena/373/newlectures/lecture5.pdf More infor
From playlist CSE373 - Analysis of Algorithms 2016 SBU