Pseudorandom number generators | Random number generation
The MIXMAX generator is a family of pseudorandom number generators (PRNG) and is based on Anosov C-systems (Anosov diffeomorphism) and Kolmogorov K-systems (Kolmogorov automorphism). It was introduced in a 1986 preprint by G. Savvidy and N. Ter-Arutyunyan-Savvidy and published in 1991. A fast implementation in C/C++ of the generator was developed by Konstantin Savvidy. The period of the generator is and the Kolmogorov entropy is for the matrix size . That generator occupies less than 2 kb, and if a smaller generator state is required, a N = 17 version with less than 200 bytes memory requirement also exists. The generator works on most 64-bit systems, including 64-bit Linux flavors and Intel Mac. It has also been tested on PPC and ARM architectures. The latest version also runs on 32-bit systems and on Windows. The generator is equally usable with , has been chosen as the default generator in CLHEP for use in Geant4 and there exists a ROOT interface. It has been recently tested extensively on very wide variety of platforms, as part of the CLHEP/Geant4 release. An analysis by L’Ecuyer, Wambergue and Bourceret, see also, showed that MIXMAX generators, just as all other Multiple Recursive Generators and linear congruential generators, has a lattice structure when the produced random numbers are considered in n - dimensional space larger than the dimension N of the matrix generator, and only in that high dimensions n > N they lie on a set of parallel hyperplanes and determined the maximum distance between the covering hyperplanes. (Wikipedia).
Tech Demo - Adaptive Voxel World - Minecraft 2.0 ?
Minecraft is everywhere on Youtube, so let's see how it could look like in the future. This is a Python implementation for the Blender game engine using GLSL. Video - http://kostackstudio.de Music - Papafiot, City Routine Tags: Minecraft, Voxel, game engine, python, programming, occlusio
From playlist Random Blender Tests
Using Blender to Add Images, Text, and Sounds to Video Clips
In this video we show how to overlay images and text on top of video clips using Blender. We also show how to add sound effects and soundtracks to the clip. Topic and Timestamps: 0:00 – Introduction 3:00 - Adding voice-overs and sound effects 5:34 - Adding image/picture overlays 11:57 -
From playlist Blender as a Video Editor
From playlist the absolute best of stereolab
Blender - New feature test: Smoke
For more information about the 3d software Blender please visit www.blender.org. www.kaikostack.com
From playlist Random Blender Tests
What is Sound? - Quickly Discover What Sound Really Is
What is Sound? This simple demonstration visually shows how sound waves are produced from a vibrating surface. A frequency generator is hooked up to a power amplifier, and the resultant signal is used to drive a loudspeaker. The signal is also sent to an oscilloscope. After listen
From playlist Physics Demonstrations
AWESOME SUPERCONDUCTOR LEVITATION!!!
A quantum levitator it's a circular track of magnets above which a razor-thin disc magically levitates, seeming to defy the laws of physics. The key to the levitator is the disc, which is made of superconducting material sandwiched between layers of gold and sapphire crystal. A piece of fo
From playlist THERMODYNAMICS
Understanding the basic reproduction number via branching process by Sujit Kumar Nath
Seminar Understanding the basic reproduction number via branching process Speaker: Sujit Kumar Nath (University of Leeds) Date: Wed, 30 September 2020, 15:00 to 16:30 Venue: Online seminar Abstract Branching process is a random process having many applications in physics, biology a
From playlist Seminar Series
Write Less Code, Generate More
Want to write less code and let the machine do the work? In this talk, Paul will give an introduction to code generation in Go, show you how to write a simple code generator, and share some tips on how to integrate code generation into your development workflow. EVENT: GopherCon UK 2019
From playlist Golang
247 - Conditional GANs and their applications
Conditional Generative Adversarial Network cGAN A GAN model generates a random image from the domain. The relationship between points in the latent space and the generated images is hard to map. A GAN can be trained so that both the generator and the discriminator models are conditioned
From playlist Generative Adversarial Networks
Generators In Python | Python Generators Explained | Python Tutorial For Beginners | Simplilearn
🔥Post Graduate Program In Full Stack Web Development: https://www.simplilearn.com/pgp-full-stack-web-development-certification-training-course?utm_campaign=GeneratorsInPython-31DoC_7IhMU&utm_medium=DescriptionFF&utm_source=youtube 🔥Caltech Coding Bootcamp (US Only): https://www.simplilearn
From playlist Python For Beginners 🔥[2022 Updated]
What Are GANs? | Generative Adversarial Networks Explained | Deep Learning With Python | Edureka
🔥 Post Graduate Diploma in Artificial Intelligence by E&ICT Academy NIT Warangal: https://www.edureka.co/executive-programs/machine-learning-and-ai This Edureka video on 'What Are GANs' will help you understand the concept of generative adversarial networks including how it works and the t
From playlist Deep Learning With TensorFlow Videos
What Are GANs? | Generative Adversarial Networks Tutorial | Deep Learning Tutorial | Simplilearn
🔥Artificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=WhatareGANs-MZmNxvLDdV0&utm_medium=Descriptionff&utm_source=youtube 🔥Professional Certificate Program In AI And Machine Learning: https://www.sim
From playlist Deep Learning Tutorial Videos 🔥[2022 Updated] | Simplilearn
Overview of Generative Adversarial Networks | AISC
For slides and more information on the paper, visit https://aisc.ai.science/events/2019-07-31 Register for the workshop here: https://www.eventbrite.ca/e/premium-hands-on-workshop-generative-adversarial-networks-and-beyond-tickets-64501555890?discount=gan_lunch Discussion lead: Andrew Mart
From playlist Generative Models
Ruby Conference 2007 Use Ruby to Generate More Ruby - RubiGen by Dr. Nic Williams
Help us caption & translate this video! http://amara.org/v/FGcz/
From playlist Ruby Conference 2007
Plug and Play Language Models: A Simple Approach to Controlled Text Generation | AISC
For slides and more information on the paper, visit https://aisc.ai.science/events/2020-01-13 Discussion lead: Raheleh Makki Discussion facilitator(s): Gordon Gibson, Royal Sequeira + Salman Mohammed Motivation: Large transformer-based language models (LMs) trained on huge text corpora
From playlist Natural Language Processing
Toward a Causal Analysis of Generalization in Deep Learning - Behnam Neyshabur
Workshop on Theory of Deep Learning: Where next? Topic: Toward a Causal Analysis of Generalization in Deep Learning Speaker: Behnam Neyshabur Affiliation:Google Date: October 18, 2019 For more video please visit http://video.ias.edu
From playlist Mathematics
Polynomials with Trigonometric Solutions (2 of 3: Substitute & solve)
More resources available at www.misterwootube.com
From playlist Using Complex Numbers