Programming with Python to solve the examples in the course "Multivariable Calculus" from Khan

Jul 16, 2024 · This article delves into the key concepts of multivariable calculus that are pertinent to machine learning, including partial derivatives, gradient vectors, the Hessian matrix, and optimization techniques.

Python 3.8.2 64-bit; To use the code make sure you have a few libaries downloaded: matplotlib; numpy; math; mpmath; scipy; tkinter; you can use these commands to download these libaries: python3 -m pip install -U pip. python3 -m pip install -U matplotlib. pip3 install numpy. pip insall mpmath. pip install scipy. pip install tkinter (The Math ...

Visualizing multivariable calculus concepts. Contribute to bkimo/Multivariable_Calculus_with_Python development by creating an account on GitHub.

Aug 7, 2024 · sympy.symbols (): It is used to declare multivariable by passing the variables as a string into its parameter. All the variables must be separated by a space forming a string. We can differentiate any sympy expression by using diff (func, var) method.

Aug 26, 2024 · Multivariable calculus deals with functions of many dimensions and/or vector valued functions. The math can be complicated to I will stick to some demonstrations here. Demonstrating sets of...

This is a one credit course to accompany Calculus 3, during which you will discover applications of multivariable calculus to data science. You will also practice programming in Python and using Arduino sensors and microprocessors to acquire data.

Mar 25, 2025 · Transform your approach to Calculus with a textbook that goes beyond traditional boundaries. Merging rigorous mathematical theory with practical, full-feature Python code, this resource empowers you to master everything from limits and derivatives to multiple integrals and multivariable optimization.

The backpropagation algorithm can be seen to be a method of organizing the multi-variable chain rule to allow for the efficient computation of many partial derivatives. Matrix calculus allows...

This toolkit is a Python-based library designed to help computations in multivariable calculus. It provides classes and methods for differentiating expressions, computing gradients, finding unit normal vectors, evaluating tangent planes, and calculating directional derivatives.