(ML 14.6) Forward-Backward algorithm for HMMs
The Forward-Backward algorithm for a hidden Markov model (HMM). How the Forward algorithm and Backward algorithm work together. Discussion of applications (inference, parameter estimation, sampling from the posterior, etc.).
From playlist Machine Learning
Investigate the forward backward algorithm of hidden Markov models, by deriving the backward algorithm using reverse mode automatic differentiation.
From playlist There and Back Again: A Tale of Slopes and Expectations (NeurIPS-2020 Tutorial)
The Backward algorithm for hidden Markov models (HMMs).
From playlist Machine Learning
(ML 14.8) Forward algorithm (part 2)
The Forward algorithm for hidden Markov models (HMMs).
From playlist Machine Learning
This is a sequel to Backpropagation 1: https://youtu.be/NLchKk9Cawg Here we mathematically justify the backpropagation algorithm.
From playlist MachineLearning
Scheduling: The Back Flow Algorithm Part 1
This lesson explains how to use the back flow algorithm and version 2 of the critical path algorithm to create a priority list. Site: http://mathispower4u.com
From playlist Scheduling
Lecture: Error and Stability of Time-stepping Schemes
The accuracy and stability of time-stepping schemes are considered and compared on various time-stepping algorithms.
From playlist Beginning Scientific Computing
(ML 14.7) Forward algorithm (part 1)
The Forward algorithm for hidden Markov models (HMMs).
From playlist Machine Learning
What Is Feedforward Control? | Control Systems in Practice
A control system has two main goals: get the system to track a setpoint, and reject disturbances. Feedback control is pretty powerful for this, but this video shows how feedforward control can make achieving those goals easier. Temperature Control in a Heat Exchange Example: http://bit.ly
From playlist Control Systems in Practice
Confusion Matrix in Machine Learning | Binary and Multiclass Classification Examples | Edureka
🔥Edureka Data Scientist Course Master Program https://www.edureka.co/masters-program/data-scientist-certification (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎") This Edureka tutorial explains the Confusion Matrix. How to construct confusion matrix for binary as well as multi class classification problems, vario
From playlist Data Science Training Videos
Forward Sensitivity Approach to dynamic data assimilation - S. Lakshmivarahan
PROGRAM: Data Assimilation Research Program Venue: Centre for Applicable Mathematics-TIFR and Indian Institute of Science Dates: 04 - 23 July, 2011 DESCRIPTION: Data assimilation (DA) is a powerful and versatile method for combining observational data of a system with its dynamical mod
From playlist Data Assimilation Research Program
Neural Networks – An overview on Torch’s nn package Full project: https://github.com/Atcold/torch-Video-Tutorials Regression example: https://github.com/Atcold/torch-Machine-learning-with-Torch Notes: 54:36 – X[i] should be X[i + j] 54:44 – Y[i] should be Y[i + j] 54:56 – Y[i] should be
From playlist Deep-Learning-Course
Lec 16 | MIT 18.085 Computational Science and Engineering I
Dynamic estimation: Kalman filter and square root filter A more recent version of this course is available at: http://ocw.mit.edu/18-085f08 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 18.085 Computational Science & Engineering I, Fall 2007
Diffusive Back and Forth Nudging algorithm - Didier Auroux
PROGRAM: Data Assimilation Research Program Venue: Centre for Applicable Mathematics-TIFR and Indian Institute of Science Dates: 04 - 23 July, 2011 DESCRIPTION: Data assimilation (DA) is a powerful and versatile method for combining observational data of a system with its dynamical mod
From playlist Data Assimilation Research Program
(IC 1.4) Source-channel separation
The source-encoder-channel-decoder-destination pipeline. Decoupling the combined encoding problem into compression (source coding) and error-correction (channel coding) via the source-channel separation theorem. A playlist of these videos is available at: http://www.youtube.com/playlist?
From playlist Information theory and Coding
Dan Crisan - Convergence of particle filters and relation to DA III
PROGRAM: Nonlinear filtering and data assimilation DATES: Wednesday 08 Jan, 2014 - Saturday 11 Jan, 2014 VENUE: ICTS-TIFR, IISc Campus, Bangalore LINK:http://www.icts.res.in/discussion_meeting/NFDA2014/ The applications of the framework of filtering theory to the problem of data assimi
From playlist Nonlinear filtering and data assimilation
Dynamics and Economy of Molecular Machines (Lecture 1) by Stefan Klumpp
PROGRAM STATISTICAL BIOLOGICAL PHYSICS: FROM SINGLE MOLECULE TO CELL ORGANIZERS: Debashish Chowdhury (IIT-Kanpur, India), Ambarish Kunwar (IIT-Bombay, India) and Prabal K Maiti (IISc, India) DATE: 11 October 2022 to 22 October 2022 VENUE: Ramanujan Lecture Hall 'Fluctuation-and-noise' a
From playlist STATISTICAL BIOLOGICAL PHYSICS: FROM SINGLE MOLECULE TO CELL (2022)
Operation Counts for Forward/Backward Substitution | Lecture 29 | Numerical Methods for Engineers
Count the number of operations required for forward and backward substitution. Join me on Coursera: https://www.coursera.org/learn/numerical-methods-engineers Lecture notes at http://www.math.ust.hk/~machas/numerical-methods-for-engineers.pdf Subscribe to my channel: http://www.youtube.
From playlist Numerical Methods for Engineers
Python Machine Learning Projects For Beginners 2023 | Machine Learning With Python | Simplilearn
🔥Artificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=PythonMachineLearningProjectsForBeginners2023&utm_medium=Descriptionff&utm_source=youtube 🔥Professional Certificate Program In AI And Machine Le