Covers vectorization in Python using Numpy for efficient scientific computing, emphasizing the benefits of avoiding for loops and demonstrating practical applications.
Explores optimizing library interactions, functionality challenges, and modularity in modern workloads, emphasizing strong boundaries between systems and instruction-level optimizations.
