From AI as a Service by Peter Elger, Eoin Shanaghy, and Johannes Ahlmann
From Machine Learning for Mortals (Mere and Otherwise) by Hefin I. Rhys
From Graph-Powered Machine Learning by Alessandro Negro
Six Questions for Kim Falk author of Practical Recommender Systems
By Frances Lefkowitz
Kim Falk is a Copenhagen-based data scientist who works with machine learning and recommender systems. Keep up with him @kimfalk on Twitter.
From Machine Learning for Business by Doug Hudgeon and Richard Nichol
In this article, you’ll see how SageMaker and the Random Cut Forest algorithm can be used to create a model that will highlight the invoice lines that Brett should query with the law firm. The result will be a repeatable process that Brett can apply to every invoice that will keep the lawyers working for his bank on their toes and will save the bank hundreds of thousands of dollars per year. Off we go!
From Data Science at Scale with Python and Dask by Jesse C. Daniel
This article delves into building linear models using Dask-ML.
From Grokking Deep Learning for Computer Vision by Mohamed Elgendy
By Mohamed Elgendy
From Grokking Deep Learning for Computer Vision By Mohamed Elgendy
From Deep Learning and the Game of Go by Max Pumperla and Kevin Ferguson
This article shows you how to use the minimax algorithm to help your game bot decide its next move.
