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Numerical Methods and Optimization in Python

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Name:Numerical Methods and Optimization in Python
Description:

Description

This course is about numerical methods and optimization algorithms in Python programming language.

*** We are NOT going to discuss ALL the theory related to numerical methods (for example how to solve differential equations etc.) – we are just going to consider the concrete implementations and numerical principles ***

The first section is about matrix algebra and linear systems such as matrix multiplication, gaussian elimination and applications of these approaches. We will consider the famous Google’s PageRank algorithm.

Then we will talk about numerical integration. How to use techniques like trapezoidal rule, Simpson formula and Monte-Carlo method to calculate the definite integral of a given function.

The next chapter is about solving differential equations with Euler’s-method and Runge-Kutta approach. We will consider examples such as the pendulum problem and ballistics.

Finally, we are going to consider the machine learning related optimization techniques. Gradient descent, stochastic gradient descent algorithm, ADAGrad, RMSProp and ADAM optimizer will be discussed – theory and implementations as well.

*** IF YOU ARE NEW TO PYTHON PROGRAMMING THEN YOU CAN LEARN ABOUT THE FUNDAMENTALS AND BASICS OF PYTHON IN THA LAST CHAPTERS ***

Section 1 – Numerical Methods Basics

   numerical methods basics
   floating point representation
   rounding errors
   performance C, Java and Python

Section 2 – Linear Algebra and Gaussian Elimination

   linear algebra
   matrix multiplication
   Gauss-elimination
   portfolio optimization with matrix algebra

Section 3 – Eigenvectors and Eigenvalues

   eigenvectors and eigenvalues
   applications of eigenvectors in machine learning (PCA)
   Google’s PageRank algorithm explained

Section 4 – Interpolation

   Lagrange interpolation theory
   implementation and applications of interpolation

Section 5 – Root Finding Algorithms

   solving non-linear equations
   root finding
   Newton’s method and bisection method

Section 6 – Numerical Integration

   numerical integration
   rectangle method and trapezoidal method
   Simpson’s method
   Monte-Carlo integration

Section 7 – Differential Equations

   solving differential-equations
   Euler’s method
   Runge-Kutta method
   pendulum problem and ballistics

Section 8 –  Numerical Optimization (in Machine Learning)

   gradient descent algorithm
   stochastic gradient descent
   ADAGrad and RMSProp algorithms
   ADAM optimizer explained

*** IF YOU ARE NEW TO PYTHON PROGRAMMING THEN YOU CAN LEARN ABOUT THE FUNDAMENTALS AND BASICS OF PYTHON IN THA LAST CHAPTERS ***

Thanks for joining my course, let’s get started!
Who this course is for:

   This course is meant for student with quantitative background or software engineers who are interested in numerical methods

Requirements

   Mathematical background – differential equations, integration and matrix algebra

Last Updated 4/2022
YouTube Video:
Category:Tutorials
Language:English  English
Total Size:3.45 GB
Info Hash:F99AB3895B1AB1E294793F19BDC2EDF421C85ACD
Added By:tutsnode Verified UploaderVIP
Date Added:2022-05-20 19:18:39
Torrent Status:Torrent Verified


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