Saturday 2 p.m.–2:30 p.m.
Effective numerical computation in Numpy and Scipy (en)
- Audience level:
- Science / 科学
This talk explores case studies of effective usage of Numpy/Scipy and shows that the computational speed sometimes improves drastically with the appropriate derivation of formulas and performance-conscious implementation. I especially focus on scipy.sparse, the module for sparse matrices, which is often useful in the areas of machine learning and natural language processing.
Although Python is considered to be a good tool for numerical computation, naive implementations can be slow and give beginners a negative impression about Python performance. With appropriate usage of Numpy/Scipy, and Python friendly derivation of formulas, computation speed can be improved dramatically. This talk covers issues discussed in  as well as practical case-studies involving algorithms typical in machine learning. I especially emphasize the convenience of scipy.sparse, the module for sparse matrices. To extract a good performance from scipy.sparse, knowledge of internal data structures and mathematical skill are necessary. This talk tries to be beginner friendly but requires familiarity with linear algebra.  Gabriete Lanaro, "Python High Performance Programming." Packt Publishing, 2013.