Lectures related to Introduction to Matplotlib

Covers advanced Excel functions and data analysis techniques, including automatic recording and using Solver.

Covers conditions, loops, functions, and graphics in R programming with practical examples.

Covers NumPy arrays and their graphical representations using Matplotlib, focusing on array creation, manipulation, and visualization techniques.

Introduces functions for input and output operations in a program.

Discusses numerical methods, focusing on stopping criteria, SciPy for optimization, and data visualization with Matplotlib.

Explores real functions, covering parity, periodicity, and polynomial functions.

Delves into functions on R^n, covering limits and partial differentiation.

Explores functions in R² with sets where x² + y² ≤ 1.

Introduces functions in R² with examples and graphical techniques, illustrated by Peter Wittwer.

Explores advanced MATLAB techniques, emphasizing vectorization, 'find' function, and plot manipulation.

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Introduces NumPy, focusing on array creation, manipulation, and its advantages for scientific computing.

Introduces Mathematica basics, including graphics creation, list manipulation, and function definition.

Covers the definition of applications, image of elements, and direct and reciprocal images.

Covers Python basics, Pandas, data manipulation, visualization, and machine learning.

Covers functions, including even and odd functions, periodicity, and function operations.

Provides a Python crash course covering basic arithmetics, complex numbers, strings, lists, dictionaries, and more.

Introduces the fundamentals of functions in microcontroller programming, emphasizing naming rules and step-by-step development.

Covers advanced Python topics like numpy operations, scipy linear algebra, and matplotlib for creating figures.

Explores differentiating under the integral sign and continuity of functions in integrals.