Explanation
Numerical Methods and Optimization in Python This course is about numerical methods and optimization algorithms in the Python programming language. We will not discuss ALL the theory related to numerical methods (for example how to solve differential equations etc.) – we will only consider the practical implementation and numerical principles The first part is about matrix algebra and linear systems such as matrices . Multiplication, Gaussian elimination and applications of these methods. We will consider Google’s famous PageRank algorithm.
Then we will talk about numerical integration. How to use techniques such as the trapezoidal rule, Simpson’s formula and the Monte-Carlo method to calculate the absolute value of a given function. The next chapter is about solving differential equations by Euler’s method and Runge-Kutta’s method. We will consider examples such as the pendulum problem and ballistics. Finally, we will consider machine learning related optimization techniques. In the next generation, the stochastic settlement algorithm, ADAGrad, RMSProp and ADAM optimizer will be discussed – theory as well as implementation.
What will you learn?
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Understand linear systems and Gaussian elimination
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Understand eigenvectors and eigenvalues
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Understand Google’s PageRank algorithm
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Understanding numerical integration
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Understand Monte-Carlo simulations
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Understand differential equations – Euler’s method and Runge-Kutta’s method
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Understand machine learning optimization algorithms (gradient gradient, stochastic gradient descent, ADAM optimizer etc.)
Who is this course for?
- This course is intended for students with a quantitative background or software engineers who are interested in numerical methods.
An Introduction to Numerical Methods and Optimization in Python
- Publisher: Udemy
- Teacher: Holzer Balazs
- Language : English
- Level : All Levels
- Number of courses: 161
- Duration : 13 hours and 58 minutes
An Introduction to Numerical Methods and Optimization in Python
Requirements
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Basic math – differential equations, integration and matrix algebra
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