From Machine Learning for Business by Doug Hudgeon and Richard Nichol
In this article, you’ll see how SageMaker and the Random Cut Forest algorithm can be used to create a model that will highlight the invoice lines that Brett should query with the law firm. The result will be a repeatable process that Brett can apply to every invoice that will keep the lawyers working for his bank on their toes and will save the bank hundreds of thousands of dollars per year. Off we go!
From Data Science at Scale with Python and Dask by Jesse C. Daniel
This article delves into building linear models using Dask-ML.
From Istio in Action by Christian Posta
This article explores the Istio gateway.
From Practical Data Science with R, 2nd Ed. By Nina Zumel and John Mount
In this article, we demonstrate some ways to get to know your data, and discuss some of the potential issues that you’re looking for as you explore.
From Spark in Action, 2nd Ed. by Jean Georges Perrin
This is the last in a series of 4 articles on the topic of ingesting data from files with Spark. This section deals with ingesting a TXT file.
From Rust in Action by Tim Mcnamara
Our strategy for this article is to use an example that compiles, then make a minor change that triggers an error which appears to emerge without any adjustment to the program’s flow. Working through the fixes to those issues should make the concepts more complete.
From Deep Learning and the Game of Go by Max Pumperla and Kevin Ferguson
This article shows you how to use the minimax algorithm to help your game bot decide its next move.
From Haskell in Depth by Vitaly Bragilevsky
This article explores text processing in the functional programming style.
From Classic Computer Science Problems in Python by David Kopec
A large number of problems which computational tools solve can be broadly categorized as constraint-satisfaction problems (CSPs). CSPs are composed of variables with possible values which fall into ranges known as domains. Constraints between the variables must be satisfied in order for constraint-satisfaction problems to be solved. Those three core concepts—variables, domains, and constraints—are simple to understand, and their generality underlies the wide applicabilit
By Jean Georges Perrin
This is the third in a series of 4 articles on the topic of ingesting data from files with Spark. This section deals with ingesting a XML file.