Mathematical optimization is a pervasive task in numerous engineering applications. It is concerned with algorithmic search for a value (usually a vector) that minimizes or maximizes a predefined objective function. In machine learning (ML), for instance, a search for the best configuration of model parameters is performed with an optimization algorithm minimizing the objective function that measures misfit between the known response values and those predicted by the ML model.
Jupyter Notebook is a great tool for data science prototyping, visualization and sharing. The first code cell of every notebook I work on contains the following commands: %matplotlib inline %load_ext autoreload %autoreload 2 These are some of the IPython Magic commands – handy enhancements added on top of the standard Python syntax to solve various tasks specific to interactive computing. The first line in the snippet above configures the notebook session to visualize and store all the Matplotlib figures inside the notebook.