From Mastering Large Datasets with Python by J.T. Wolohan
This article covers
· Using map to do complex data transformations
· Chaining together small functions into pipelines
· Applying these pipelines in parallel on large datasets
From Deep Learning for Vision Systems by Mohamed Elgendy
In this part, we will take a look at feature extraction—a core component of the computer vision pipeline.
From Human-in-the-Loop Machine Learning by Robert (Munro) Monarch
From Fighting Churn with Data by Carl Gold
From Graph Databases in Action by Dave Bechberger and Josh Perryman
In this article, we’ll review what makes a problem a good graph use case. We’ll start by examining a few general categories of problems and discussing why they might make for good graph use case. Finally, we’ll analyze a general framework that we can use to help us decide if our problem is a good graph use case.
From Grokking Machine Learning by Luis G. Serrano
From Mastering Large Datasets by JT Wolohan
This article explores using the map function creatively in a data project.
From Machine Learning with R, tidyverse, and mlr by Hefin Rhys
This article covers:
• What the tidyverse is
• What’s meant by tidy data
• How to install and load the tidyverse
• How to use the tibble, dplyr, ggplot2 and tidyr packages of the tidyverse
From Azure Storage, Streaming, and Batch Analytics
By Richard L. Nuckolls
