Improve NumPy vs Numba vs JAX lecture clarity and output formatting

[jstac]

Summary

This PR improves the readability and clarity of the NumPy vs Numba vs JAX comparison lecture:

• Adds formatted output with .6f precision for all numerical results
• Simplifies the race condition explanation, removing verbose details
• Improves code flow with better print statement placement
• Enhances readability of parallel Numba examples
• Clarifies JAX vmap benefits and use cases
• Removes redundant multithreading note from NumPy section
• Strengthens conclusion about tool trade-offs

Test plan

• Converted to Python via jupytext and executed successfully
• All code cells run without errors
• Output displays correctly formatted results (0.999998)
• Race condition example shows expected -inf result

🤖 Generated with Claude Code

[jstac]

Tested locally by converting the markdown to Python with jupytext and executing. All code cells run successfully with correct output formatting. The race condition example properly demonstrates the issue with the parallel implementation showing -inf as expected.

[github-actions]

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