Piter Py #4

 

Jupyter Notebooks (previously IPython Notebooks) has already become the product of choice for many scientific and education projects, ranging from particle physics and cosmology to data science and all kinds or tutorials. For majority of notebook users, it works like (semi-)interactive document: one may combine descriptive text (with Markdown formatting), executable code, and computation results, including plots and images and save the notebook for future non-interactive work. On the other hand, Jupyter notebooks support various kinds of interactive widgets, ranging from geographic maps to complex dashboards, resulting in specialized web interface. In my talk, I will explain how these two kinds of usage are reflected in the notebook architecture and discuss how to select the approach for your product.

 

Belarus. Minsk

Lead Developer

 

Nikolay Karelin uses Python since 2007. He is currently working for CIB Software as Lead Developer of Deep Learning team and uses Python to implement AI-based solutions for advanced document processing tasks. Before joining CIB team, he worked on various positions in both academia and industry with main focus on numerical simulation and data processing. He holds Ph.D. degree in physics (optics).