From Testing JavaScript Applications by Lucas da Costa
This article explains what parallelism is and how Jest can help you run your tests faster.
From Math for Programmers by Paul Orland
This article covers
● Modeling algebraic expressions as data structures in Python
● Writing code to analyze, transform, or evaluate an algebraic expression
● Building a data structure from elements and combinators
From Transfer Learning for Natural Language Processing by Paul Azunre
This article discusses getting started with baselines and generalized linear models.
From Classic Computer Science Problems in Java by David Kopec
From Software Telemetry by Jamie Riedesel
Exploring Natural Language Processing is a free eBook with chapters selected by Hobson Lane.
The Cool Way to Search Text
By Scott Penbertht and Chris Mattmann
| Andrew Ferlitsch reveals new paradigms–and patterns–for automated deep learning
Andrew Ferlitsch, from the developer relations team at Google Cloud AI, is so far out on the cutting edge of machine learning and artificial intelligence that he has to invent new terminology to describe what’s happening in Cloud AI with Google Cloud’s enterprise clients. In this interview with editors at Manning Publications, he talks about the current and coming changes in machine learning systems, starting with the concept of model amalgamation. Ferlitsch is currently writing a book, Deep Learning Design Patterns, which collects his ideas along with the most important composable model components. |
From Self-Sovereign Identity by Drummond Reed & Alex Preukschat
This article delves into the constituent parts of Self-Sovereign Identity (SSI), and how they work together.