How to pick a machine learning model 3: Choosing a loss function
Part of the End-to-End Machine Learning School course library at http://e2eml.school See these concepts used in an End to End Machine Learning project: https://end-to-end-machine-learning.teachable.com/p/polynomial-regression-optimization/ Watch the rest of the How to Choose a Model serie
From playlist E2EML 171. How to Choose Model
What Are Error Intervals? GCSE Maths Revision
What are error Intervals and how do we find them - that's the mission in this episode of GCSE Maths minis! Error Intervals appear on both foundation and higher tier GCSE maths and IGCSE maths exam papers, so this is excellent revision for everyone! DOWNLOAD THE QUESTIONS HERE: https://d
From playlist Error Intervals & Bounds GCSE Maths Revision
Standard Error of the Estimate used in Regression Analysis (Mean Square Error)
An example of how to calculate the standard error of the estimate (Mean Square Error) used in simple linear regression analysis. This typically taught in statistics. Like us on: http://www.facebook.com/PartyMoreStud... Link to Playlist on Regression Analysis http://www.youtube.com/cour
From playlist Linear Regression.
Introduction to the Confusion Matrix in Classification
In this introduction, we give you a brief overview of what a confusion matrix is, how to create your matrix, and why you should use it. A confusion matrix, also known as an error matrix, uses a special table to help visualize the performance of your algorithm. That way, you can easily se
From playlist Data Science in Minutes
Brief intro the the linear regression formula and errors.
From playlist Regression Analysis
Comparison of systematic and random error. Types of systematic error, including offset error and scale factor error/
From playlist Experimental Design
Tangent plane approximation and error estimation
Free ebook http://tinyurl.com/EngMathYT This lecture shows how to use tangent plane techniques to approximate complicated functions. We also discuss how to estimate the errors involved.
From playlist Mathematics for Finance & Actuarial Studies 2
Model-Based Design for Predictive Maintenance, Part 5: Development of a Predictive Model
See the full playlist: https://www.youtube.com/playlist?list=PLn8PRpmsu08qe_LVgUHtDrSXiNz6XFcS0 After performing real-time tests and validating your algorithm, you can use it to detect whether there are any mechanical or electrical issues in your system. However, you can also use condition
From playlist Model-Based Design for Predictive Maintenance
Linear Regression using Python
This seminar series looks at four important linear models (linear regression, analysis of variance, analysis of covariance, and logistic regression). A video that explains all four model types is at https://www.youtube.com/watch?v=SV9AxXFWZnM&t=12s This video is on linear regression usin
From playlist Statistics
DDPS | Model reduction with adaptive enrichment for large scale PDE constrained optimization
Talk Abstract Projection based model order reduction has become a mature technique for simulation of large classes of parameterized systems. However, several challenges remain for problems where the solution manifold of the parameterized system cannot be well approximated by linear subspa
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Stanford Seminar - Flash Reliability in Production: The Expected and the Unexpected
"Flash Reliability in Production: The Expected and the Unexpected" - Bianca Schroeder of University of Toronto About the talk: As solid state drives based on flash technology are becoming a staple for persistent data storage in data centers, it is important to understand their reliabilit
From playlist Engineering
DRAM Errors in the Wild: A Large-Scale Field Study
(October 21, 2009) Bianca Schroeder of the University of Toronto Computer Science Department gives an in depth discussion on how common dynamic random access memory errors are, their statistical properties, and how they are affected by external and chip-specific factors. Stanford Univer
From playlist Engineering
Value at Risk (VaR) Backtest (FRM T5-04)
When we specify something like a 95% value at risk or 95% VaR, we mean that 95% is the confidence level and, therefore, 5% is the significance level. That means we expect on 5% of days for the actual loss to be worse than the VaR or to exceed the VaR. This video is about the backtest of a
From playlist Market Risk (FRM Topic 5)
Evaluating Performance of Large Language Models with Linguistics - Deep Random Talks S2E5
Listen to Amir Feizpour, Serena McDonnell and Bai Li speak about Natural Language Processing on DRT's this episode.
From playlist Deep Random Talks- Season 2
Machine Learning: Testing and Error Metrics
Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoyt A friendly journey into the process of evaluating and improving machine learning models. - Training, Testing - Evaluation Metrics: Accuracy, Precision, Recall, F1 Score - Type
From playlist General Machine Learning
Andreas Savin - Beyond density functional approximations by lessons from density functional theory
Recorded 13 April 2022. Andreas Savin of Sorbonne Université presents "Getting beyond density functional approximations by using lessons from density functional theory" at IPAM's Model Reduction in Quantum Mechanics Workshop. Abstract: Joint work with Jacek Karwowski, Yvon Maday, Etienne P
From playlist 2022 Model Reduction in Quantum Mechanics Workshop
Grammatical Error Correction for Legal Professionals
Want to build products like this? visit https://ai.science/mvp Motivation: Manual Grammatical Error Correction takes a significant amount of time. The automation of this process will allow for people to spend more time working on actual solutions.
From playlist Community Projects
CMU Neural Nets for NLP 2017 (19): Document Level Models
This lecture (by Zhengzhong Liu) for CMU CS 11-747, Neural Networks for NLP (Fall 2017) covers: * Models of Coreference * Discourse Parsing * Document Level Prediction Slides: http://phontron.com/class/nn4nlp2017/assets/slides/nn4nlp-19-document.pdf Previous Video: https://youtu.be/EKvk
From playlist CMU Neural Nets for NLP 2017
Anh-Huy Phan: "Chain Tensor Network: Instability and how to deal with it"
Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021 Workshop III: Mathematical Foundations and Algorithms for Tensor Computations "Chain Tensor Network: Instability and how to deal with it" Anh-Huy Phan - Skolkovo Institute of Science and Technology Abstract:
From playlist Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021
How to calculate margin of error and standard deviation
In this tutorial I show the relationship standard deviation and margin of error. I calculate margin of error and confidence intervals with different standard deviations. Playlist on Confidence Intervals http://www.youtube.com/course?list=EC36B51DB57E3A3E8E Like us on: http://www.facebook
From playlist Confidence Intervals