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 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 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.