Sometimes you need to nest a pattern in another pattern. Learn how to build these patterns and then extract information from them. https://teacher.desmos.com/activitybuilder/custom/605e21d90925ca0c93fabbbd
From playlist Pattern Matching with Computation Layer
Pattern Matching - Being Flexible
As your patterns become more complex you'll need to build patterns that can match expressions with different but similar forms. Activity Link: https://teacher.desmos.com/activitybuilder/custom/60626999811e664d596ece18
From playlist Pattern Matching with Computation Layer
Exploring an amazing pattern that forms when we multiply numbers built only with the one digit
From playlist Number Patterns
ArrrrCamp 2014- Patterns, patterns everywhere
By, Grzegorz Witek They're everywhere. They're on the leaf that falls from the tree straight on your head. They're on the building you pass everyday morning. They're on the socks you wear today and in the code you write. Patterns. Design patterns are defined as general, reused solutions t
From playlist ArrrrCamp 2014
Graph Data Structure 1. Terminology and Representation (algorithms)
This is the first in a series of videos about the graph data structure. It mentions the applications of graphs, defines various terminology associated with graphs, and describes how a graph can be represented programmatically by means of adjacency lists or an adjacency matrix.
From playlist Data Structures
Matching and parsing fractions using the pattern library in Computation Layer https://teacher.desmos.com/activitybuilder/custom/60593e26c77e9949be1edfe6 Fraction Bars: https://teacher.desmos.com/activitybuilder/custom/605b3f91fa93fb0d4aa99b2b#preview/1bd9a17d-726f-43f4-94bc-a3715fe67a87
From playlist Pattern Matching with Computation Layer
Data structures: Introduction to graphs
See complete series on data structures here: http://www.youtube.com/playlist?list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P In this lesson, we have described Graph data structure as a mathematical model. We have briefly described the concept of Graph and some of its applications. For practice
From playlist Data structures
Get the Code: http://goo.gl/X3Kxc Welcome to my Composite Design Pattern Tutorial! The Composite design pattern is used to structure data into its individual parts as well as represent the inner workings of every part of a larger object. The composite pattern also allows you to treat bot
From playlist Java Video Tutorial
Nonlinear approximation by deep ReLU networks - Ron DeVore, Texas A&M
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
Nicolas Behr - Tracelet Algebras
Stochastic rewriting systems evolving over graph-like structures are a versatile modeling paradigm that covers in particular biochemical reaction systems. In fact, to date rewriting-based frameworks such as the Kappa platform [1] are amongst the very few known approaches to faithfully enco
From playlist Combinatorics and Arithmetic for Physics: 02-03 December 2020
Deep Learning: History, Motivation, and Evolution of Deep Learning
The lecturer discusses the motivation behind deep learning. He begins with the history and inspiration of deep learning. Then he discusses the history of pattern recognition and introduces gradient descent and its computation by backpropagation. Finally, he discusses the hierarchical repre
From playlist Machine Learning
Lecture 2.3: Josh Tenenbaum - Computational Cognitive Science Part 3
MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015 View the complete course: https://ocw.mit.edu/RES-9-003SU15 Instructor: Josh Tenenbaum Exploring how humans learn new concepts and make intelligent inferences from little experience. Using probabilistic generative models
From playlist MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015
AMMI 2022 Course "Geometric Deep Learning" - Lecture 3 (Geometric Priors I) - Taco Cohen
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 3: Symmetries • Abstract groups • Symmetry groups • Gro
From playlist AMMI Geometric Deep Learning Course - Second Edition (2022)
Clojure Conj 2012 - Machine Learning Live
Machine Learning Live by: Mike Anderson This presentation will introduce a new start-up focused on machine learning that is using Clojure. We will discuss the technology and architecture it has developed for real-time machine learning and pattern recognition, and how Clojure provides a un
From playlist Clojure Conf 2012
WSU Master Class: From Chemistry to Living Materials: Matter & Sound with Markus Buehler
Professor Markus Buehler discusses how scientists can design new proteins that can revolutionize materials engineering, through manipulating vibrations that have been changed from matter into sound and from sound back into matte This lecture was recorded on November 13, 2019, at the World
From playlist WSU Master Classes
Data and Mathematics, or Mathematics and Data
Jürgen Jost, Max Planck Institute for Mathematics in the Sciences, Germany
From playlist Public Lectures
Hodge theory, coniveau and algebraic cycles - Claire Voisin
Claire Voisin Centre national de la recherche scientifique; Distinguished Visiting Professor, School of Mathematics October 6, 2014 My talk will be a broad introduction to what is the (mostly conjectural) higher dimensional generalization of Abel's theorem on divisors on Riemann surfaces,
From playlist Mathematics
Introduction to SNA. Lecture 6. Network structure and visualization
K-core decomposition of networks. Diads and triads. Edge reciprocity. Frequent subgraphs. Network motifs. Assortative mixing. Network visualization. Forde directed layouts. Adjacency matrix ordering. Lecture slides: http://www.leonidzhukov.net/hse/2015/sna/lectures/lecture6.pdf
From playlist Introduction to SNA
Using Machine Learning and Breaking Rocket Motors
Let's take on Siraj Raval's 100 day challenge to learn a little bit about machine learning every day. This video explains some machine learning I've done with composite rocket motor casings using #AcousticEmissions. #100DaysOfMLCode Original Video - https://youtu.be/cuQMBj1cWPo You can
From playlist SciJoy Uploads