This repository contains an advanced tutorial on optimizing Python code for machine learning applications, focusing on processing large amounts of data efficiently. It covers three powerful libraries: Numba, NumPy, and Polars.
Key features:
- In-depth guide to Numba's JIT compilation for high-performance computing
- Advanced NumPy techniques for efficient array operations
- Introduction to Polars for fast data manipulation of large datasets
- Parallel processing strategies on CPUs
- GPU acceleration using CUDA
- Custom data types for complex ML algorithms
- Profiling and optimization strategies
Topics covered:
- Numba: JIT compilation, parallel processing, GPU acceleration
- NumPy: Vectorization, advanced indexing, and array operations
- Polars: Fast data frame operations, lazy evaluation, and parallel execution
Ideal for ML researchers and data scientists looking to dramatically improve computational efficiency, reduce execution times from hours to minutes, and handle larger datasets with ease. Dive in to supercharge your Python code for machine learning and data processing tasks!