High-Performance Computing with Python 3.x
Get to grips with various techniques and tools to provide High-Performance Computing in Python 3.x
Created by Mohammed Kashif
Explore practical ways to boost Python performance for demanding computing tasks. Learn how to speed up code, handle large data, and build responsive applications using Python's powerful libraries and tools. Gain hands-on experience with real techniques used in high-performance computing.
Packt | Feb 2019 | 252 min
What You Will Learn
You'll work through practical exercises that show how to optimize Python code for speed and efficiency. Step-by-step guidance helps you use libraries like NumPy, SciPy, and Dask to handle complex computations and large datasets. Each topic is introduced with clear examples so you can apply what you learn right away.
Key Features
- Speed up numerical computations using NumPy, SciPy, Cython, and Numba
- Apply parallel processing and distributed computing with multiprocessing and Dask
- Build responsive and robust applications using reactive programming in Python
Target Audience
If you're a Python programmer, data analyst, or aspiring data scientist with basic Python skills, this course is designed for you. It's ideal if you want to scale your code, improve performance, or work on projects that require heavy computation and large-scale data processing.
