Jupyter Notebooks for Numerical Analysis

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Related lectures (26)

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Numerical Analysis: Introduction to Computational Methods

Covers the basics of numerical analysis and computational methods using Python, focusing on algorithms and practical applications in mathematics.

Scientific Computing with Python

Covers the basics of scientific computing with Python, focusing on programming, algorithms, and numerical methods.

Numerical Methods: Bisection and Multidimensional Arrays

Discusses the bisection method for solving nonlinear equations and its implementation using Python with NumPy and Matplotlib.

Root Finding Methods: Bisection and Secant Techniques

Covers root-finding methods, focusing on the bisection and secant techniques, their implementations, and comparisons of their convergence rates.

Classical Mechanics and Molecular Dynamics

Covers classical mechanics, molecular dynamics, integrators, constant-temperature simulations, and melting point calculations for aluminum.

Introduction to NumPy: Basics of Scientific Computing

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

Numerical Methods: Stopping Criteria, SciPy, and Matplotlib

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

Data Science Essentials: Pandas, Numpy, Matplotlib

Introduces Pandas, Numpy, and Matplotlib for data analysis and visualization in Python.

Data Science with Python: Numpy Basics

Introduces the basics of Numpy, a numerical computing library in Python, covering advantages, memory layout, operations, and linear algebra functions.

Nonlinear Equation Resolution: Introduction to Bisection Method

Introduces the bisection method for resolving nonlinear equations using numerical techniques and Python programming.