This folder contains Jupyter Notebook tutorials covering various Python libraries and tools. Each tutorial is designed to provide an introduction and practical examples to help you get started.
- Python Basics: Introduction to Python syntax, variables, and basic programming concepts.
- NumPy: Used for numerical computing and handling large arrays and matrices.
- SciPy: Advanced scientific computing, including optimization, integration, and differential equations.
- Matplotlib: Plotting graphs and visualizations.
- SymPy: Symbolic mathematics, such as algebraic manipulations and solving equations.
- Pandas: Data manipulation and analysis, especially for tabular data.
- JAX and Theano: For high-performance machine learning and automatic differentiation.
- SymEngine: Lightweight symbolic computation library for fast algebraic operations.
- PyAutoDiff: Tools for automatic differentiation in Python.
- FEniCS: Solving PDEs using finite element methods.
- Dedalus: Framework for solving differential equations in spectral space.
- FiPy: Solving PDEs with finite volume methods.
Progress Tracker:
- Python Basics
- [] NumPy
- [] SciPy
- [] Matplotlib
- [] Sympy
- [] Pandas
- [] JAX and Theano
- [] SymEngine
- [] PyAutoDiff
- [] FEniCS
- [] Dedalus
- [] FiPy