All About Bloom Filters

From Algorithms and Data Structures for Massive Datasets by Dzejla Medjedovic, Emin Tahirovic, and Ines Dedovic

This article covers:

·  Learning what Bloom filters are, why and when they are useful

·  Understanding how Bloom filters work

·  Configuring a Bloom filter in a practical setting

·  Exploring the interplay between Bloom filter parameters

Shallow Transfer Learning in NLP

From Transfer Learning for Natural Language Processing by Paul Azunre

This article delves into using shallow transfer learning to improve your NLP models.

Key exchange standards

From Real-World Cryptography by David Wong

In this article, the author teaches readers about the Diffie-Hellman key exchange standard, which was the very first key exchange algorithm ever invented, and the Elliptic Curve Diffie-Hellman key exchange standard, which is the Diffie-Hellman built with elliptic curves.

Getting Started with Baselines

From Transfer Learning for Natural Language Processing by Paul Azunre

This article discusses getting started with baselines and generalized linear models.

Sharpen your Java and compsci skills

From Classic Computer Science Problems in Java by David Kopec

The Cool Way to Search Text

The Cool Way to Search Text

By Scott Penbertht and Chris Mattmann

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

Introducing Edge Computing

From Making Sense of Edge Computing by Cody Bumgardner

Conceptually, edge computing is concerned with when it’s best to migrate computational functionally toward source of data and when it is best to move the data itself. This abstract concept of function versus data migration drives not only the fundamental motivations of edge computing, but also the broader field of distributed systems. The act of distributing processes makes even the simplest tasks more complicated.

Using AI to Get Business Results

From Succeeding with AI by Veljko Krunic

This article discusses how to get practical business results from AI and how this book will help you learn how to do it.

Transparent and understandable AI systems

From Interpretable AI by Ajay Thampi

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