2.1 Introduction 2.1 Introduction This chapter will provide brief explanations of the fundamental geographic data models: vector and raster.We will introduce the theory behind ...

Publishing Scientific Results Ready-to-go demos Handling datasets and results Available archival / preprint servers or services Data storage and preservation Planning data stora...

Welcome Citation Info Front Cover Welcome 🇺🇸 🇧🇷 🇨🇳 Welcome! This is an open source and open access book on how to do Data Science using Julia . Our target audience are r...

Chapter 2 Interpretability Chapter 2 Interpretability There is no mathematical definition of interpretability. A (non-mathematical) definition I like by Miller (2017)3 is: Int...

7.9 Exercises References 7.9 Exercises List and describe three types of vector, raster, and geodatabase formats. Name at least two differences between read_sf() and the mo...

Beauty Is in Simplicity Beauty Is in Simplicity There is one quote that I think is particularly good for all software developers to know and keep close to their hearts: Beaut...

6.6. Random Projection 6.6.1. The Johnson-Lindenstrauss lemma 6.6.2. Gaussian random projection 6.6.3. Sparse random projection 6.6. Random Projection The sklearn.random_pro...

Deep Learning Is for Everyone Deep Learning Is for Everyone A lot of people assume that you need all kinds of hard-to-find stuff to get great results with deep learning, but as...

NCAR: Hydrological Modeling Who am I? What problem am I trying to solve? How does Dask help? Why we chose Dask Pain points Technology we use around Dask Other thoughts N...