10_performance_optimization

Module: Performance and Optimization

📋 Description

Optimize the performance of your Python applications. Profiling, benchmarking, optimization techniques, Cython, NumPy vectorization, caching, and algorithmic complexity analysis.

Key concepts: Profiling (cProfile, py-spy), benchmarking, memory optimization, Cython, Numba JIT, vectorization, caching.

🎯 Learning Objectives

• Objective 1
• Objective 2
• Objective 3

📚 Content (14 Topics)

1. Profiling Cprofile
2. Line Profiler
3. Pyspy Profiling
4. Benchmarking Timeit
5. Pytest Benchmark
6. Optimization Techniques
7. Cython Basics
8. Numpy Vectorization
9. Numba Jit
10. PyPy Intro
11. Lazy Evaluation
12. Caching LRU
13. Memory Optimization
14. Algorithmic Complexity

⏱️ Estimated Total Time

28-42 hours

🚀 Recommended Order

Follow the numerical order for a logical progression.