Subject

Data Science

Modern Data Solutions with Python

From Python for Big Datasets by John T. Wolohan


The Inner Workings of Spark

spark_in_act

From Spark in Action, Second Edition by Jean George Perrin

The Magic of Graphs and Machine Learning

PMachineLearning-MM
From Graph-Powered Machine Learning by Alessandro Negro

The Random Cut Forest Algorithm

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!

Building Linear Models with Dask ML

From Data Science at Scale with Python and Dask by Jesse C. Daniel

This article delves into building linear models using Dask-ML.

Handling Data with R

From Practical Data Science with R, 2nd Ed. By Nina Zumel and John Mount

In this article, we demonstrate some ways to get to know your data, and discuss some of the potential issues that you’re looking for as you explore.

Slideshare: Finding Valuable Insights in Complex Data

From Graph Databases in Action by Dave Bechberger


slideshare-finding-valuable-insights-in-complex-data

How Does Computer Vision Work?

From Grokking Deep Learning for Computer Vision by Mohamed Elgendy

human_and_artificial_sensing
By Mohamed Elgendy

A Match Made in Heaven

From Deep Learning for Natural Language Processing By Stephan Raaijmakers

slideshare-a-match-made-in-heaven

 

The Guide to Computer Vision

From Grokking Deep Learning for Computer Vision By Mohamed Elgendy

slideshare-the-guide-to-computer-vision

© 2019 Manning — Design Credits