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
Kernel Recipes 2015 - How to choose a kernel for your products? - by Willy Tarreau
It’s often difficult to select a kernel for products that are shipped to customers. Several branches exist, bugs need to be avoided as much as possible and updates must be rare enough not to upset customers. All this must be true during all the product’s lifecycle. This presentation will s
From playlist Kernel Recipes 2015
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
Introduction to the Kernel and Image of a Linear Transformation
This video introduced the topics of kernel and image of a linear transformation.
From playlist Kernel and Image of Linear Transformation
Proof that the Kernel of a Linear Transformation is a Subspace
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Proof that the Kernel of a Linear Transformation is a Subspace
From playlist Proofs
Determine the Kernel of a Linear Transformation Given a Matrix (R3, x to 0)
This video explains how to determine the kernel of a linear transformation.
From playlist Kernel and Image of Linear Transformation
Kernel Recipes 2014 - The Linux Kernel, how fast it is developed and how we stay sane doing it
This talk will go into the latest statistics for the development of the Linux kernel. It will describe how the many thousand developers all work together and are able to release a stable kernel every 3 months with no planning. Finally an informal discussion between Greg KH and Willy Tarr
From playlist Kernel Recipes 2014
Kernel Recipes 2016 : kernelci.org: 1.5 million kernel boots (and counting) - Kevin Hilman
The kernelci.org project performs over 2000 kernel boot tests per day for upstream kernels on a wide variety of hardware. This talk will provide an overview of kernelci.org, how distributed board farms are used, how it is used by kernel maintainers and developers, and how you can make use
From playlist Kernel Recipes 2016
The Bergman kernel of the polydisk and the ball
I compute the Bergman kernel of the unit polydisk and the unit Euclidean ball. For my previous video on the Bergman kernel see https://www.youtube.com/watch?v=loIC28LNgNM
From playlist Several Complex Variables
Machine Learning Lecture 25 "Kernelized algorithms" -Cornell CS4780 SP17
Lecture Notes: http://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote13.html http://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote14.html
From playlist CORNELL CS4780 "Machine Learning for Intelligent Systems"
Martin Larsson: Affine Volterra processes and models for rough volatility
Abstract: Motivated by recent advances in rough volatility modeling, we introduce affine Volterra processes, defined as solutions of certain stochastic convolution equations with affine coefficients. Classical affine diffusions constitute a special case, but affine Volterra processes are n
From playlist Probability and Statistics
Data Science with Mathematica -- LibraryLink, CUDA, CUDALink, and CUDA through LibraryLink
In this video of the Data Science with Mathematica track I provide a very rudimentary introduction to LibraryLink, the CUDALink package, and the use of CUDA through LibraryLink, the latter being my preference, as it offers the greatest flexibility, and one can use all the features of your
From playlist Data Science with Mathematica
Machine Learning Lecture 26 "Gaussian Processes" -Cornell CS4780 SP17
Cornell class CS4780. (Online version: https://tinyurl.com/eCornellML ) GPyTorch GP implementatio: https://gpytorch.ai/ Lecture Notes: http://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote15.html Small corrections: Minute 14: it should be P(y,w|x,D) and not P(y|x,w,D) sorry
From playlist CORNELL CS4780 "Machine Learning for Intelligent Systems"
How to Enter a Kaggle Competition (using Kernels) | Kaggle
Ever wanted to try out Kaggle competitions but weren't sure how to go about it? In this video Kaggle data scientist Rachael walks you through how to enter a competition to help you start your climb up the leaderboard! SUBSCRIBE: https://www.youtube.com/c/kaggle?sub_confirmation=1&utm_medi
From playlist Getting Started on Kaggle | Kaggle
CNN: Convolutional Neural Networks Explained - Computerphile
Years of work down the drain, the convolutional neural network is a step change in image classification accuracy. Image Analyst Dr Mike Pound explains what it does. Kernel Convolutions: https://youtu.be/C_zFhWdM4ic Deep Learning: https://youtu.be/l42lr8AlrHk Botnets: https://youtu.be/UV
From playlist Neural Networks
Data Science with Mathematica -- J/Link
In this session of my Data Science with Mathematica track I discuss several features of the J/Link package, and in both direction: calling into Java from M and calling into M from Java. I start with several simple examples of non-visual and visual classes and progress towards several Java
From playlist Data Science with Mathematica
Machine Learning with Scikit-learn - Data Analysis with Python and Pandas p.6
How to include the Pandas data analysis library into your machine learning workflow. Text-based tutorial: https://pythonprogramming.net/machine-learning-python3-pandas-data-analysis/ Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://disco
From playlist Data Analysis w/ Python 3 and Pandas
Multivariate Portfolio Choice via Quantiles
SIAM Activity Group on FME Virtual Talk Series Join us for a series of online talks on topics related to mathematical finance and engineering and running every two weeks until further notice. The series is organized by the SIAM Activity Group on Financial Mathematics and Engineering. Spea
From playlist SIAM Activity Group on FME Virtual Talk Series
Data Visualization and Exploration with Python || Stephen Elston
Visualization is an essential method in any data scientist’s toolbox and is a key data exploration method and is a powerful tool for presentation of results and understanding problems with analytics. Attendees are introduced to Python visualization packages, Matplotlib, Pandas, and Seaborn
From playlist Python