From Deep Learning for Vision Systems by Mohamed Elgendy
In this part, we will take a look at feature extraction—a core component of the computer vision pipeline.
From Human-in-the-Loop Machine Learning by Robert (Munro) Monarch
From Fighting Churn with Data by Carl Gold
From Graph Databases in Action by Dave Bechberger and Josh Perryman
In this article, we’ll review what makes a problem a good graph use case. We’ll start by examining a few general categories of problems and discussing why they might make for good graph use case. Finally, we’ll analyze a general framework that we can use to help us decide if our problem is a good graph use case.
From Deep Learning for Vision Systems by Mohamed Elgendy
In this part, we will delve into image preprocessing for computer vision systems.
From Grokking Machine Learning by Luis G. Serrano
Six Questions for Kevin Ferguson, co-author of Deep Learning and the Game of Go.
Kevin Ferguson and Max Pumperla are deep learning specialists skilled in distributed systems and data science. Together, they built the open source bot BetaGo. They also both count Max, the hero of the movie Pi, as a major influence. “He’s a talented mathematician who slowly loses his mind over the stock market and has an intense relationship with his power tools. That’s essentially my short bio,” says Pumperla.
From Deep Learning for Vision Systems by Mohamed Elgendy
In this part, we will discuss the input images for computer vision systems.
From Deep Learning for Vision Systems 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.
