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Take the Pain out of Managing Serverless Apps

From Knative in Action by Jacques Chester

Learn Machine Learning by Making Projects

From Machine Learning Bookcamp by Alexey Grigorev

Newly-Introduced JUnit 5 Annotations and Classes

From JUnit in Action, Third Edition by Catalin Tudose

This article discusses some of the most important new additions in Junit 5.

 

Real-World Devops

From Operations Anti-Patterns: DevOps Solutions by Jeffery D. Smith

Leverage the Power of Machine Learning

From Machine Learning with TensorFlow, Second Edition by Chris A. Mattmann

Managing a Successful AI Project, Part 1: setting actionable goals

From Succeeding with AI by Veljko Krunic

Focused human involvement and attention makes or breaks an AI project. This article discusses the first questions you should ask in the context of every AI project, as well as the most important ideas to keep in mind during any AI project.

A Pre-Trained Model that Fakes It until It Makes It

From Deep Learning with PyTorch by Eli Stevens and Luca Antiga

This article explores the use and capabilities of GANs, using a fictional (and inadvasible) example of aspiring art forgers.

What is a Penetration Tester?

From Penetrating Enterprise Networks by Royce Davis

This article describes penetration testing: what it is and why a company might want a penetration test. We will also delve into the job of the penetration tester.

Making Better Unit Tests: part 1, the AAA pattern

From Unit Testing, Principles, Practices, and Patterns by Vladimir Khorikov

In this article I’ll give you a refresher on some basic topics. I’ll go over the structure of a typical unit test, which is usually represented by the Arrange-Act-Assert pattern. I’ll also show the unit testing framework of my choice — xUnit — and explain why I’m using it and not one of its competitors.

Improving Mad Libs with Expressions

From Terraform in Action by Scott Winkler

This article discusses how we can improve a Mad Libs generator using expressions.

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