From Grokking Deep Learning for Computer Vision by Mohamed Elgendy
In this part, we will discuss the input images for computer vision systems.
From Grokking Deep Learning for Computer Vision by Mohamed Elgendy
In this article, we’ll zoom in on the interpreting device component (of a computer vision system) to take a look at the pipeline it uses to process and understand images.
Chapters selected by Jeff Smith
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
Six Questions for Jonathan Carroll, author of Beyond Spreadsheets with R
By Frances Lefkowitz
Jonathan Carroll is a data science consultant providing R programming services. He holds a PhD in theoretical physics.
From Spark in Action, Second Edition by Jean George Perrin
From Graph-Powered Machine Learning by Alessandro Negro
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!
