sympy

Uses SymPy to symbolically derive the binomial-to-Poisson distribution limit and perform maximum likelihood estimation with Fisher information for the normal distribution.

Introduces the basics of SymPy, covering symbolic expansion, factorization, equation solving, differentiation, integration, and matrix operations with simple examples.

Explores how to simulate basic quantum computing operations with SymPy, including qubit notation, bra-ket representation, and tensor products.

Introduces basic SymPy usage for symbolic computation in Python, covering rational numbers, pi, exponentials, and other fundamental numeric types.