CppCon 2017: Fedor Pikus “C++ atomics, from basic to advanced. What do they really do?”
http://CppCon.org — Presentation Slides, PDFs, Source Code and other presenter materials are available at: https://github.com/CppCon/CppCon2017 — C++11 ...
CppCon
CS480/680 Lecture 12: Gaussian Processes
Pascal Poupart
StatQuest: Fiitting a curve to data, aka lowess, aka loess
If you can fit a line, you can fit a curve! I've even got example R code on the StatQuest GitHub: ...
StatQuest with Josh Starmer
Lecture 10 - Decision Trees and Ensemble Methods | Stanford CS229: Machine Learning (Autumn 2018)
Take an adapted version of this course as part of the Stanford Artificial Intelligence Professional Program. Learn more at: https://stanford.io/3bhmLce Raphael ...
stanfordonline
(June 2017) Emmanuel Farhi "A Model of the International Monetary System" - PSE
Paris School of Economics
CT6 Introduction to generalised linear models (GLMs)
One of the 125 units that make up the CT6 (Statistical Methods) Online Classroom available from ActEd (The Actuarial Education Company). For more ...
Actuarial Education
A Primer on Gaussian Processes for Regression Analysis || Chris Fonnesbeck
Gaussian processes are flexible probabilistic models that can be used to perform Bayesian regression analysis without having to provide pre-specified ...
Coding Tech
On the critic function of implicit generative models - Arthur Gretton
Seminar on Theoretical Machine Learning Topic: On the critic function of implicit generative models Speaker: Arthur Gretton Affiliation: University College ...
Institute for Advanced Study
Nonparametric tests (SPSS)
Currell: Scientific Data Analysis. Nonparametric tests. See also 6.4.6 http://ukcatalogue.oup.com/product/9780198712541.do © Oxford University Press.
Oxford Academic (Oxford University Press)
4 блок вопросов - физика атомного ядра и элементарных частиц - комментарии
Модели атомных ядер, от капельной до гидродинамических коллективных моделей. Ещё можно упомянуть оптическую модель ядра, а основной акцент ...
Евгений Голыгин
Uncertainty Quantification of Nonlinear Systems
Arbitrary Polynomial Chaos for Uncertainty Quantification of Correlated Random Variables in Nonlinear Systems Traditional sample-based uncertainty ...
APMonitor.com
Di Chen: End-to-End learning for the Deep Multivariate Probit Model
Di Chen, Cornell University Sep 21, 2018 Title: End-to-End learning for the Deep Multivariate Probit Model CompSust Open Graduate Seminar (COGS) ...
CompSustNet
1D Convolutional Neural Networks for Time Series Modeling - Nathan Janos, Jeff Roach
PyData LA 2018 This talk describes an experimental approach to time series modeling using 1D convolution filter layers in a neural network architecture.
PyData
Fitting Bayesian Random-Effects Models Using PROC MCMC
Fang Chen in the Statistical Applications Department at SAS explains a new feature in SAS/STAT 9.3 - fitting Bayesian random-effects models Using PROC ...
SAS Software
Using An Expanded Cyber Kill Chain Model to Increase Attack Resiliency
by Sean Malone The Cyber Kill Chain model provides a framework for understanding how an adversary breaches the perimeter to gain access to systems on the ...
Black Hat
Kaggle Reading Group: XLNet | Kaggle
This week we're starting a new paper in the Kaggle reading group: XLNet: Generalized Autoregressive Pretraining for Language Understanding (Yang et al, ...
Kaggle
Containers unplugged: understanding user namespaces - Michael Kerrisk
User namespaces are at the heart of many interesting technologies that allow isolation and sandboxing of applications, for example running containers without ...
NDC Conferences
Lecture 16 - Radial Basis Functions
Radial Basis Functions - An important learning model that connects several machine learning models and techniques. Lecture 16 of 18 of Caltech's Machine ...
caltech
Machine Learning Tutorial: From Beginner to Advanced
Explore the fundamentals behind machine learning, focusing on unsupervised and supervised learning. You'll learn what each approach is, and you'll see the ...
MATLAB
Reconciling modern machine learning and the bias-variance trade-off
It turns out that the classic view of generalization and overfitting is incomplete! If you add parameters beyond the number of points in your dataset, generalization ...
Yannic Kilcher
Detecting Financial Fraud at Scale with Machine Learning - Elena Boiarskaia (H2O ai)
Detecting fraudulent patterns at scale is a challenge given the massive amounts of data to sift through, the complexity of the constantly evolving techniques, and ...
Databricks
Neural Network Architectures and Deep Learning
This video describes the variety of neural network architectures available to solve various problems in science ad engineering. Examples include convolutional ...
Steve Brunton
Alexandre Tkatchenko: "Towards a Unified Machine Learning Model of Molecular Chemical Space"
Machine Learning for Physics and the Physics of Learning 2019 Workshop I: From Passive to Active: Generative and Reinforcement Learning with Physics ...
Institute for Pure & Applied Mathematics (IPAM)
Cannon's Algorithm Part-2 Explained with Generalized Example in Hindi
Cannon's Algorithm Part-1 https://youtu.be/ZaNxMTjUB0w GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ...
5 Minutes Engineering
Inside TensorFlow: Summaries and TensorBoard
Take an inside look into the TensorFlow team's own internal training sessions--technical deep dives into TensorFlow by the very people who are building it!
TensorFlow
StatQuest: Linear Discriminant Analysis (LDA) clearly explained.
LDA is surprisingly simple and anyone can understand it. Here I avoid the complex linear algebra and use illustrations to show you what it does so you will know ...
StatQuest with Josh Starmer
NLP Tutorial 5 - Rule Based Text Phrase Extraction and Matching using SpaCy in NLP
In this video, we will learn about Rule-Based Text Phrase Extraction and Matching using SpaCy in NLP. spaCy is a free, open-source library for advanced ...
KGP Talkie
On the critic function of implicit generative models - Arthur Gretton
Seminar on Theoretical Machine Learning Topic: On the critic function of implicit generative models Speaker: Arthur Gretton Affiliation: University College ...
Institute for Advanced Study
tinyML Talks Local Manu Rastogi: Tutorial on micro-kernel based hardware acceleration
tinyML Talks local Webcast - recorded August 13, 2020 "Tutorial on micro-kernel based hardware acceleration" Manu Rastogi Energy and compute are both ...
tinyML
Aileen Nielsen - Irregular time series and how to whip them
PyData London 2016 This talk will present best-practices and most commonly used methods for dealing with irregular time series. Though we'd all like data to ...
PyData
Multi-kernel Linear Mixed Models for Complex Phenotype Prediction
Linear mixed models (LMMs) and their extensions have recently become the method of choice in phenotype prediction for complex traits. However, LMM use to ...
Microsoft Research
Back to Basics: The Abstract Machine - Bob Steagall - CppCon 2020
https://cppcon.org/ ...
CppCon
How to Explain Models with IntepretML Deep Dive
With the recent popularity of machine learning algorithms such as neural networks and ensemble methods, etc., machine learning models become more like a ...
Microsoft Developer
The mother of all representer theorems for inverse problems & machine learning - Michael Unser
This workshop - organised under the auspices of the Isaac Newton Institute on “Approximation, sampling and compression in data science” — brings together ...
The Alan Turing Institute
JuliaCon 2018 | ARCH Models in Julia | Simon Broda
Volatility modeling lies at the heart of much of financial risk management. The workhorse model in this field is the GARCH model, along with its various ...
The Julia Programming Language
Fourier Transform Explained
In this video, the concept of Fourier Transform is explained from a communication perspective. By watching this video, you will learn the following topics: 0:00 ...
ALL ABOUT ELECTRONICS
Nonparametric series regression
Stata's -npregress series- estimates nonparametric series regression using a B-spline, spline, or polynomial basis. Nonparametric regression is agnostic about ...
StataCorp LLC
Views on Physics - David Gross
NatiFest: September 16, 2016 "Views on Physics"* by David Gross sns.ias.edu/natifest More videos on http://video.ias.edu.
Institute for Advanced Study
Functional Kernel Analysis of Neural Networks (ft. Arthur Jacot)
Functional kernel analysis allows a nice analysis of neural networks in the infinite-width limit, as discussed by Arthur Jacot, PhD candidate in mathematics at ...
ZettaBytes, EPFL
CUDA Kernel ll Writing and Launching Explained in 5 Minutes (Hindi)
GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING SUBJECT :- Discrete Mathematics (DM) Theory Of ...
5 Minutes Engineering
Attention for time series forecasting & COVID predictions - Isaac Godfried
Self-attention and the transformer architecture have broken many benchmarks and enabled widespread progress in NLP. However, at this point neither ...
Weights & Biases
C/C++ vs Security! - Gynvael Coldwind - code::dive 2018
From a security researcher's point of view both C and C++ are wonderful! They are full of pitfalls, traps, quirks and unexpected behaviors. To make things even ...
code::dive conference
