From API Security in Action by Neil Madden
This article covers the definition of an API and what it means for an API to be secure.
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 Seriously Good Software by Marco Faella
This article delves into using memory storage space efficiently, thus making your software more efficient and resilient.
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 Probabilistic Deep Learning with Python by Oliver Dürr, Beate Sick, and Elvis Murina
This article dives into neural network architectures and how get started implementing and using them.
From Docker in Action, Second Edition by Jeff Nickoloff and Stephen Kuenzli
This article gives you a rundown of what Docker in and what problems it solves.
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 Dependency Injection, Principles, Practices, and Patterns by Steven van Deursen and Mark Seemann
This articles explains the Singleton Lifestyle: what it is and what it means for your code.
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 Elm in Action by Richard Feldman
This article deals with writing fuzz tests in Elm.