The video introduces Numba, a library that enhances Python's numerical code performance using a Just-In-Time (JIT) compiler. It contrasts Python's convenience with its slower speed compared to languages like C and Fortran. Numba allows Python functions to be compiled into machine code at runtime, offering significant speed improvements with minimal code changes. Examples include a Monte Carlo simulation and Conway's Game of Life, demonstrating Numba's ability to accelerate computations. The video also discusses Numba's compilation strategies, including 'no Python' mode for maximum optimization.

What is Numba and how does it improve Python's performance?

Explain the tradeoff between using Python and languages like C or FORTRAN for mathematical operations.

Describe the process of how Numba compiles Python functions to machine code.

What are the benefits of using the JIT decorator in Numba?

How does Numba handle code that cannot be optimized for speed?