From Probabilistic Deep Learning with Python by Oliver Dürr, Beate Sick, and Elvis Murina
From Spark in Action, Second Edition by Jean-Georges Perrin
From Machine Learning for Business by Doug Hudgeon and Richard Nichol
This article covers basic time-series forecasting: what it is and why it’s a tough problem.
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
From Fighting Churn with Data by Carl Gold
From Graph Databases in Action by Dave Bechberger
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 Build Your Career in Data Science by Emily Robinson and Jacqueline Nolis
