Operator theory | Hilbert space
In machine learning, tree kernels are the application of the more general concept of positive-definite kernel to tree structures. They find applications in natural language processing, where they can be used for machine-learned parsing or classification of sentences. (Wikipedia).
What are Graph Kernels? Graph Kernels explained, Python + Graph Neural Networks
The abundance of graph-structured data and need to perform machine learning ML tasks on this data led to development of graph kernels. Machine Learning, Deep Learning. Graph kernels, this means kernel functions between graphs, have been proposed in the 2010s to solve the problem of assess
From playlist Learn Graph Neural Networks: code, examples and theory
Kernel Recipes 2018 - Knowing the definition of Linux kernel to...- Vaishali Thakkar
Self learning is underrated in the modern era of education. While kernel being the heart of an operating system, traditional universities [in India] are still far away from teaching anything more than the definition of Linux Kernel. The talk will mostly focus on my journey of self learning
From playlist Kernel Recipes 2018
Tree Graphs - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Introduction to Spanning Trees
This video introduces spanning trees. mathispower4u.com
From playlist Graph Theory (Discrete Math)
Kernel Recipes 2014 - Quick state of the art of clang
Working on clang for a while now, I will propose a review of my work on debian rebuild and comment results.
From playlist Kernel Recipes 2014
Kernels Introduction - Practical Machine Learning Tutorial with Python p.29
In this machine learning tutorial, we introduce the concept of Kernels. Kernels can be used with the Support Vector Machine in order to take a new perspective and hopefully allow us to translate into further dimensions in order to find a linearly separable case. https://pythonprogramming
From playlist Machine Learning with Python
We don't know what a tree is (and this video won't tell you)
Offset your carbon footprint with Wren! They'll protect 5 extra acres of rainforest for each of the first 100 people who sign up at https://www.wren.co/join/minuteearth. It turns out that defining what is and isn't a “tree” is way harder than it seems. LEARN MORE ************** To learn m
From playlist This Is Not A Playlist
Approximation Algs. - Lecture 19
All rights reserved for http://www.aduni.org/ Published under the Creative Commons Attribution-ShareAlike license http://creativecommons.org/licenses/by-sa/2.0/ Tutorials by Instructor: Shai Simonson. http://www.stonehill.edu/compsci/shai.htm Visit the forum at: http://www.coderisland.c
From playlist ArsDigita Algorithms by Shai Simonson
Kernel Recipes 2017 - Linux Kernel release model - Greg KH
This talk describes how the Linux kernel development model works, what a long term supported kernel is, and why all Linux-based systems devices should be using all of the stable releases and not attempting to pick and choose random patches. It also goes into how the kernel community appro
From playlist Kernel Recipes 2017
Richard Gustavson, Manhattan College
April 26, Richard Gustavson, Manhattan College Developing an Algebraic Theory of Integral Equations
From playlist Spring 2022 Online Kolchin seminar in Differential Algebra
Ngoc Mai Tran: Stochastic geometry to generalize the Mondrian process
The Mondrian process is a stochastic process that produces a recursive partition of space with random axis-aligned cuts. Random forests and Laplace kernel approximations built from the Mondrian process have led to efficient online learning methods and Bayesian optimization. By viewing the
From playlist Workshop: High dimensional spatial random systems
Kernel Recipes 2022 - Checking your work: validating the kernel by building and testing in CI
The Linux kernel is one of the most complex pieces of software ever written. Being in ring 0, bugs in the kernel are a big problem, so having confidence in the correctness and robustness of the kernel is incredibly important. This is difficult enough for a single version and configuration
From playlist Kernel Recipes 2022
Statistical Rethinking 2022 Lecture 16 - Gaussian Processes
Slides and other course materials: https://github.com/rmcelreath/stat_rethinking_2022 Intro: https://www.youtube.com/watch?v=uYNzqgU7na4 Music: https://www.youtube.com/watch?v=kXuasY8pDpA Music: https://www.youtube.com/watch?v=eTtTB0nZdL0 Pause: https://www.youtube.com/watch?v=pxPdsqrQByM
From playlist Statistical Rethinking 2022
Kernel Recipes 2015 - Linux Stable Release process - by Greg KH
The Linux kernel gets a stable release about once every week. This talk will go into the process of getting a patch accepted into the stable releases, how the release process works, and how Greg does a review and release cycle. It will consist of live examples of patches submitted to be a
From playlist Kernel Recipes 2015
Kernel Recipes 2017 - Container FS interfaces - James Bottomley
Many talks about containers start with Orchestration systems like Docker or Kubernetes. However, this one will look at the storage impacts on the actual in-kernel container API. With the addition of the superblock namespace (essentially a user namespace for the kernel to filesystem boundar
From playlist Kernel Recipes 2017
Kernel Recipes 2022 - Test-driven kernel releases
Upstream Linux kernel testing has grown exponentially on many fronts during the past few years: kselftest is now more stable, KUnit gaining coverage and many out-of-tree test suites have kept growing. Many automated systems are running those tests continuously and regzbot has now become a
From playlist Kernel Recipes 2022
Why Kernels - Practical Machine Learning Tutorial with Python p.30
Once we've determined that we can use Kernels, the next question is of course why would we bother using kernels when we can use some other function to transform our data into more dimensions. The point of using Kernels is to be able to perform a calculation (inner product in this case) in
From playlist Machine Learning with Python
Kernel Recipes 2015 - Introduction to Kernel Power Management - by Kevin Hilman
In order to keep up with the complexities of SoCs, the Linux kernel has an ever-growing set of features for power management. For the uninitiated, it can be confusing how each of these features work and even more confusing how they should work together. This talk will be a high-level intro
From playlist Kernel Recipes 2015
