RailsConf 2017: Distributed Tracing: From Theory to Practice by Stella Cotton
RailsConf 2017: Distributed Tracing: From Theory to Practice by Stella Cotton Application performance monitoring is great for debugging inside a single app. However, as a system expands into multiple services, how can you understand the health of the system as a whole? Distributed tracing
From playlist RailsConf 2017
The (Full)stack Trace: Understand Your App with Distributed Tracing - Will Klein - JSConf US 2019
Original Title: Follow the (full)stack trace: understand your app with distributed tracing What if we could follow a user request, from a page load in the browser, to our backend, through our services, to the database, and back? What does it look like to see how our UI receives data and r
From playlist JSConf US 2019
Continuous Probability Distributions - Basic Introduction
This statistics video tutorial provides a basic introduction into continuous probability distributions. It discusses the normal distribution, uniform distribution, and the exponential distribution. The probability is equal to the area under the curve and the total area under the curve is
From playlist Statistics
Multiple-Scattering Microfacet BSDFs with the Smith Model
The paper "Multiple-Scattering Microfacet BSDFs with the Smith Model" is available here: https://eheitzresearch.wordpress.com/240-2/ Update: it is being added to Blender's Cycles! - https://developer.blender.org/D2002 Modeling multiple scattering in microfacet theory is considered an imp
From playlist Light Transport, Ray Tracing and Global Illumination (Two Minute Papers)
Indexing 17: distributed search
Instead of using MapReduce to construct a single index, we can distribute portions of the index across a cluster of machines. We can then send a query to all the machines, receive partial ranked lists and then combine them into one list that would be returned to the user. This is known as
From playlist IR7 Inverted Indexing
Cumulative Distribution Functions and Probability Density Functions
This statistics video tutorial provides a basic introduction into cumulative distribution functions and probability density functions. The probability density function or pdf is f(x) which describes the shape of the distribution. It can tell you if you have a uniform, exponential, or nor
From playlist Statistics
New: Two-Tailed Feature for All Distributions!
📣 Our probability calculator now has a two-tailed option for all distributions! 📣 https://www.geogebra.org/classic#probability
From playlist New Features and Releases
(ML 7.7.A1) Dirichlet distribution
Definition of the Dirichlet distribution, what it looks like, intuition for what the parameters control, and some statistics: mean, mode, and variance.
From playlist Machine Learning
Separable Subsurface Scattering | Two Minute Papers #66
Separable Subsurface Scattering is a novel technique to add real-time subsurface light transport calculations for computer games and other real-time applications. ____________________________ The paper "Separable Subsurface Scattering" and its implementation is available here: https://us
From playlist Two Minute Papers
Lecture 19: Variance Reduction (CMU 15-462/662)
Full playlist: https://www.youtube.com/playlist?list=PL9_jI1bdZmz2emSh0UQ5iOdT2xRHFHL7E Course information: http://15462.courses.cs.cmu.edu/
From playlist Computer Graphics (CMU 15-462/662)
TU Wien Rendering #33 - Metropolis Light Transport
Metropolis Light Transport is a powerful technique that can outperform the convergence speed of Bidirectional Path Tracing on most difficult scenes (what makes a scene difficult is a story on its own). It promises optimal importance sampling "along multiple steps" in the stationary distrib
From playlist TU Wien Rendering / Ray Tracing Course
Lecture 18: Monte Carlo Rendering (CMU 15-462/662)
Full playlist: https://www.youtube.com/playlist?list=PL9_jI1bdZmz2emSh0UQ5iOdT2xRHFHL7E Course information: http://15462.courses.cs.cmu.edu/
From playlist Computer Graphics (CMU 15-462/662)
Lecture 13: Spatial Data Structures (CMU 15-462/662)
Full playlist: https://www.youtube.com/playlist?list=PL9_jI1bdZmz2emSh0UQ5iOdT2xRHFHL7E Course information: http://15462.courses.cs.cmu.edu/
From playlist Computer Graphics (CMU 15-462/662)
Project: Blue-steel ray tracer | MIT 6.189 Multicore Programming Primer, IAP 2007
Project: Blue-steel ray tracer License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 6.189 Multicore Programming Primer, January (IAP) 2007
How Ray Tracing (Modern CGI) Works And How To Do It 600x Faster
In which we explore ray tracing, the reason modern CGI can look so convincing, and ReSTIR, a recent technique that allows images (and particularly animations) to be rendered hundreds of times faster. RIS Paper: https://diglib.eg.org/bitstream/handle/10.2312/EGWR.EGSR05.139-146/139-146.pdf
From playlist Summer of Math Exposition 2 videos
TU Wien Rendering #15 - Rendering Equation Properties
Equipped with the knowledge of BRDFs used for the two most common materials, we get a step closer to solve the Holy Rendering Equation. According to its infinite dimensional and singular properties, it is immensely difficult to solve, but to our surprise, with a few more tricks up our slee
From playlist TU Wien Rendering / Ray Tracing Course
Lecture 16: The Rendering Equation (CMU 15-462/662)
Full playlist: https://www.youtube.com/playlist?list=PL9_jI1bdZmz2emSh0UQ5iOdT2xRHFHL7E Course information: http://15462.courses.cs.cmu.edu/
From playlist Computer Graphics (CMU 15-462/662)
Raytracing and raymarching simulations of non-euclidean geometries - Henry Segerman
Workshop on Topology: Identifying Order in Complex Systems Topic: Raytracing and raymarching simulations of non-euclidean geometries Speaker: Henry Segerman Affiliation: Oklahoma State University Date: December 4, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
(ML 10.2) Posterior for linear regression (part 1)
How to compute the posterior distribution for the weight vector w under a Bayesian model for linear regression.
From playlist Machine Learning