#dirty data

Data quality issues will cost companies approx. 15% to 25% of total revenue. Any lack of diligence towards any of the key areas will lead to a compromise in data quality & eventually give rise to the dirty data. Let’s learn more about the checkpoints to address data quality issue.

Dirty Data / Nursing Informatics Reading and Sharing

Data are classified as “dirty” when the database contains errors that render the data inaccurate. This compromises the data integrity. Dirty data may result from human errors in entering data, such as misspelling a name or incorrectly entering an ID number. Dirty data may also result from viruses, worms, or other bugs installed into a system. Hackers may enter a system and alter or remove data.…

How to JOIN Dirty Data in SQL Server

In this puzzle, we’re going to learn how to join dirty data using SQL Server.  Many times you’re forced to use someone else’s data.  Since it is their system and data, not yours, you can’t always fix the inconsistently formatted data before it is in the database.

So how can you use it?  Do this puzzle and learn how!

Solving puzzles is a great way to learn SQL.  Nothing beats practicing what…

One way to visualize what goes on is to turn the network upside down and ask it to enhance an input image in such a way as to elicit a particular interpretation. Say you want to know what sort of image would result in “Banana.” Start with an image full of random noise, then gradually tweak the image towards what the neural net considers a banana. By itself, that doesn’t work very well, but it does if we impose a prior constraint that the image should have similar statistics to natural images, such as neighboring pixels needing to be correlated. 

in, “Inceptionism: Going Deeper into Neural Networks,” Google Research Blog, June 17, 2015