Bias and Fairness in Machine Learning, Part 3: building a bias-aware model

From Feature Engineering Bookcamp by Sinan Ozdemir

This article series covers

●      Recognizing and mitigating bias in our data and model

●      Quantifying fairness through various metrics

●      Applying feature engineering techniques to remove bias from our model without sacrificing model performance

Free liveProject – Build an Extensible CLI with Python Build an Extensible CLI with Python Don’t miss out! Offer ends November 28, 2021. Only available to new email subscribers. $49.99 FREE! Get your free liveProject! Take on the challenge of designing and building a custom CLI using Python…. Continue Reading →

On this day 09/07 On this day… iPod Nano Introduced September 7 2005 The iPod Nano was introduced today in 2005, replacing the highly popular iPod Mini. Rather than the hard drive used by the Mini, the iPod Nano utilized flash storage which… Continue Reading →

Free eBook – HTML 5 in Action HTML 5 in Action Don’t miss out! Offer ends June 20, 2021. Only available to new email subscribers. $31.99 FREE! Get your free eBook! “The go-to guide for all things HTML5.” – Tyson Maxwell, RaytheonHTML5 in Action provides a… Continue Reading →

The What and Why of Domain-Specific Languages

From Domain-Specific Languages Made Easy by Meinte Boersma A domain-specific language is a software language that allows domain experts to capture their knowledge in a precise enough way to make that executable. The following article is a standalone excerpt from… Continue Reading →

Free eBook – NodeJS in Action NodeJS in Action, 2nd Edition Don’t miss out! Offer ends October 12, 2020. Only available to new email subscribers. $39.99 FREE! Get your free eBook! “The definitive guide” – William E. WheelerNode.js in Action, Second Edition starts at square… Continue Reading →

When Machine Learning Becomes Machine Design: new paradigms and patterns for automated deep learning

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.

Read more author interviews here


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Dynamic Linking: a crash course

From WebAssembly in Action by C. Gerard Gallant

This article covers

§ How dynamic linking works for WebAssembly modules

§ Why you might want to use dynamic linking and why you might not

§ How to create WebAssembly modules as main or side modules

§ What the different options are for dynamic linking and how to use each approach


Server-Rendered Web Apps with React and Next.js

From Next.js in Action by Adam Boduch

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