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Optimal Control by Prof. G.D. Ray,Department of Electrical Engineering,IIT Kharagpur.For more details on NPTEL visit ...

Lecture 22 : Tutorial Session - 4: Python implementation of Jacobi Method.

This video is part of the Mooc “Machine learning in Python with scikit-learn ” hosted on FUN platform: ...

In this video we are going to take a look at logistic regression. Unlike linear regression, analysis of variance (ANOVA), and ...

Parameter Estimation, Slope , Intercept, ANOVA, Residual, R-square.

Mamba is a new neural network architecture that came out this year, and it performs better than transformers at language ...

Lecture 13 : Tutorial Session - 2: Python Coding for Naive Gaussian Elimination Method.

Lecture 15: Regression.

Course website: http://bit.ly/DLSP20-web Playlist: http://bit.ly/pDL-YouTube Speaker: Alfredo Canziani Week 1: ...

Learn Data Science is this full tutorial course for absolute beginners. Data science is considered the "sexiest job of the 21st ...

Week 4: Tutorial 04 Part 01: Plotting In 1 D Using Python.

Session 04: Mastering Searching Algorithms | Linear Search vs Binary Search Welcome to the 4th session of our 25-part series!

Lecture 1 : Introduction.

All lesson resources are available at http://course.fast.ai.) In this lesson, we dive into backpropagation and the creation of a simple ...

00:00:00 - Introduction 00:01:59 - Linear model and neural net from scratch 00:07:30 - Cleaning the data 00:26:46 - Setting up a ...

00:00 Introduction and survey 01:36 "Lesson 0" How to fast.ai 02:25 How to do a fastai lesson 04:28 How to not self-study 05:28 ...

Week 07: Lecture 34: Demand Forecasting using AI/ML: Building Regression Tree Model using Python.

Advanced Numerical Analysis by Prof. Sachin C. Patwardhan,Department of Chemical Engineering,IIT Bombay.For more details ...

10 cases help you to solving linear equations by computer program how to do the interpolation but that is somehow against ...

Heteroscedastic Residual, Multiple Linear Regression, ANOVA, K-fold cross-validation, Model adequacy.