The Art Of Visualizing High Dimensional Data Vincent Granville by Vincent Granville instant download after payment.

I discuss different techniques to produce professional data videos, animated GIFs, and other visualizations in Python, using the pillow and moviepy libraries. Applications include visualizing prediction intervals regardless of the number of features (also called independent variables), supervised classification applied to an infinite dataset, convergence of machine learning algorithms, and animations featuring objects of various sizes moving at various speeds according to various paths. For instance, I show a video simulation of 300 comets circling the sun, to assess the risk of a collision.

The Python libraries in question allow for low-level image processing at the pixel level. This is particularly useful to build ad-hoc, original visualization algorithms. I also discuss optimization: amount of memory required, performance of compression techniques, numpy versus math library, anti-aliasing to depixelate an image, and so on. Some of the videos use the RGBA palette format. This 4-dimensional color encoding (red, green, blue, alpha) allows you to set the transparency level (also called “opacity”) when objects overlap. It is particularly useful in models involving mixtures or overlapping groups in supervised classification. In that context, not only it helps with visualizations, but it actually solves the classification problem on its own.