http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Representing multivariate random signals using principal components. Principal component analysis identifies the basis vectors that describe the la
From playlist Random Signal Characterization
Linear regression is used to compare sets or pairs of numerical data points. We use it to find a correlation between variables.
From playlist Learning medical statistics with python and Jupyter notebooks
Sampling (4 of 5: Introductory Examples of Stratified Random Sampling)
More resources available at www.misterwootube.com
From playlist Data Analysis
This lesson introduces the different sample methods when conducting a poll or survey. Site: http://mathispower4u.com
From playlist Introduction to Statistics
How to Choose a SAMPLING Method (12-7)
When possible, use probability sampling methods, such as simple random, stratified, cluster, or systematic sampling.
From playlist Sampling Distributions in Statistics (WK 12 - QBA 237)
Statistics Lecture 3.3: Finding the Standard Deviation of a Data Set
https://www.patreon.com/ProfessorLeonard Statistics Lecture 3.3: Finding the Standard Deviation of a Data Set
From playlist Statistics (Full Length Videos)
Statistics Lesson #1: Sampling
This video is for my College Algebra and Statistics students (and anyone else who may find it helpful). It includes defining and looking at examples of five sampling methods: simple random sampling, convenience sampling, systematic sampling, stratified sampling, cluster sampling. We also l
From playlist Statistics
Statistics Lesson #3: Randomized Experiments & Observational Studies
This video is for my College Algebra and Statistics students (and anyone else who may find it helpful). I define a randomized experiment, show a couple of examples, and define some important vocabulary related to experiments. Then I define an observational study, give an example, and discu
From playlist Statistics
Kaggle Reading Group : An Open Source AutoML Benchmark | Kaggle
This week we're starting a new paper: An Open Source AutoML Benchmark by Gijsbers et al from the 2019 ICML Workshop on Automated Machine Learning. Paper: https://www.automl.org/wp-content/uploads/2019/06/automlws2019_Paper45.pdf Benchmark website: https://openml.github.io/automlbenchmark/
From playlist Kaggle Reading Group | Kaggle
Cascadia Ruby 2014- The Science of Success
By, Davy Stevenson Software is approached mainly from the angle of engineering. Let's step back and take a look at software as science. How can we increase the quality of our code, tune our minds to efficiently solve problems, and correctly reapply known solutions to new problems? Learn a
From playlist Cascadia Ruby 2014
RubyConf 2018 - Practical guide to benchmarking your optimizations by Anna Gluszak
RubyConf 2018 - Practical guide to benchmarking your optimizations by Anna Gluszak Many people believe that ruby applications are inherently slow, yet oftentimes it is the lack of optimization and not the language that is at fault. But how do you even get started with this daunting task o
From playlist RubyConf 2018
Descending through a Crowded Valley -- Benchmarking Deep Learning Optimizers (Paper Explained)
#ai #research #optimization Deep Learning famously gives rise to very complex, non-linear optimization problems that cannot be solved analytically. Therefore, the choice of a suitable optimization algorithm can often make or break the training of a Deep Neural Network. Yet, the literature
From playlist Papers Explained
AQC 2016 - An Optimal Stopping Approach for Benchmarking Probabilistic Optimizers
A Google TechTalk, June 27, 2016, presented by Walter Vinci (USC) ABSTRACT: We propose a strategy for benchmarking probabilistic optimizers based on an optimal stopping approach. We seek to optimize both the objective function and the number of calls to the solver. A crucial advantage of
From playlist Adiabatic Quantum Computing Conference 2016
On the numerical integration of the Lorenz-96 model... - Grudzien - Workshop 2 - CEB T3 2019
Grudzien (U Nevada in Reno, USA) / 13.11.2019 On the numerical integration of the Lorenz-96 model, with scalar additive noise, for benchmark twin experiments ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook
From playlist 2019 - T3 - The Mathematics of Climate and the Environment
Redesigning over-the-counter financial markets 2/2
Darrell Duffie Stanford University, USA
From playlist Distinguished Visitors Lecture Series
#21. Finding the Sample Size Needed to Estimate a Population Proportion using StatCrunch
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys #21. Finding the Sample Size Needed to Estimate a Population Proportion using StatCrunch
From playlist Statistics Final Exam
Welcome to Quantitative Risk Management (QRM). There is so much confusion about tails, that it is time to clarify what we are speaking about. Heavy tails, long tails and fat tails are not the same thing from a statistical and probabilistic point of view. In mathematics we need to be preci
From playlist Quantitative Risk Management
Cascadia Ruby 2014- Speed up Rails, Speed up Your Code
By Aaron Patterson. Let's talk about speed! In this talk, we'll examine ways that we've been speeding up Rails for the next release. We'll look at techniques used for speeding up database interaction as well as view processing. Techniques for finding bottlenecks and eliminating them will b
From playlist Cascadia Ruby 2014
Statistics Lecture 6.3: The Standard Normal Distribution. Using z-score, Standard Score
https://www.patreon.com/ProfessorLeonard Statistics Lecture 6.3: Applications of the Standard Normal Distribution. Using z-score, Standard Score
From playlist Statistics (Full Length Videos)
Automating Machine Learning | Data Science Institute
ABOUT THE TALK: Recent years have seen a widespread adoption of machine learning in industry and academia, impacting diverse areas from advertisement to personal medicine. As more and more areas adopt machine learning and data science techniques, the question arises on how much expertise
From playlist talks