This is the problem with Convolutional Neural Networks as well. CNN is good at detecting features, but will wrongly activate the neuron for face detection. This is because it is less effective at exploring the spatial relationships among features.
A simple CNN model can extract the features for nose, eyes and mouth correctly but will wrongly activate the neuron for the face detection. Without realizing the mis-match in spatial orientation and size, the activation for the face detection will be too high.
Read on to see how capsule networks can help solve issues with convolutional neural networks.
What I’m doing is simply converting the table into its JSON form, and then using this to create a table using the multi-row VALUES syntax which paradoxically allows expressions. The expression I’m using is JSON_Value, which allows me do effectively dictate the source within the table, via that JSON Path expression, and the destination. As it is an expression, I can do all sorts of manipulation as well as a transpose. I could, if I wanted, (in SQL 2017)provide that path parameter as a variable. This sort of technique can be used for several other reporting purposes, and it is well-worth experimenting with it because it is so versatile.
That is not at all what I would have thought up; very interesting approach. I’d probably just be lazy and shell out to R Services.
After you’ve created your knowledge base you can then edit and update your knowledge base. There’s a few different ways to update your knowledge.
a. Manually edit the knowledge base directly within QnAMaker.ai. You can do this by directly editing the questions by modifying the text of your knowledge base.
b. Edit the source of your knowledge base. Click the Settings tab on the left to edit the URL of your FAQs or upload a new document.
Building a bot is pretty easy, and Dustin shows you just how to do it.
The Archive access tier in blob storage was made generally available today (13th December 2017) and with it comes the final piece in the puzzle to archiving data from the data lake.
Where Hot and Cool access tiers can be applied at a storage account level, the Archive access tier can only be applied to a blob storage container. To understand why the Archive access tier can only be applied to a container, you need to understand the features of the Archive access tier. It is intended for data that has no or low SLAs for availability within an organisation and the data is stored offline (Hot and Cool access tiers are online). Therefore, it can take up to 15 hours for data to be made online and available. Brining Archive data online is a process called rehydration (fitting for the data lake). If you have lots of blob containers in a storage account, you can archive them and rehydrate them as required, rather than having to rehydrate the entire storage account.
Read on for more details, including a pattern for archiving data lake data.
R has been available for Windows since the very beginning, but if you have a Windows machine and want to use R within a Linux ecosystem, that’s easy to do with the new Fall Creator’s Update (version 1709). If you need access to the gcc toolchain for building R packages, or simply prefer the bash environment, it’s easy to get things up and running.
Once you have things set up, you can launch a bash shell and run R at the terminal like you would in any Linux system. And that’s because this is a Linux system: the Windows Subsystem for Linux is a complete Linux distribution running within Windows. This page provides the details on installing Linux on Windows, but here are the basic steps you need and how to get the latest version of R up and running within it.
Click through for a quick tutorial.
Our ERP database has been chosen by the IT gods to get moved to the shiny new flash storage array, off the old spinning-rust SAN. This is fantastic news for the business users. But lo, the executives warn us, “You must do this with no downtime!” (said in my best Brent Ozar PHB-imitation voice). Of course when we tell them that’s impossible, they say, “OK, you must do this with minimal downtime.” That’s mo’ betta’.
So what are our typical options for doing a database migration? Or, more specifically, a data file migration. See, we’re not moving to a new server, and we’re not moving a bunch of databases together; we’re just moving this one ERP database. And we’re keeping it on the same SQL instance, we’re just swapping the storage underneath.
Click through for some discussion on options, followed by implementation of a particular strategy.
We can take those names and R types, string them together, and “convert” them to SQL data types. (Mapping data types from one language to another is waaaay outside the scope of this post. Lines 11-13 are quick and dirty, just for demonstration purposes. Okie dokie?)
It’s certainly a less than ideal solution, but it does the job. And I hope as well that someday this functionality ends up being something built into the product.
Viewing decrypted data within SQL Server Management Studio (SSMS) is very easy. SSMS uses .NET 4.6 and the modern SQL Server client, so you can pass in the necessary encryption options. SSMS uses the connection string to access the Master Key and return the data in its decrypted format.
First create a new SQL Connection and Click Options to expand the window.
Then go to the Additional Connections Parameters Tab of the login window and simply type column encryption setting = enabled. Then choose Connect.
Click through to see the whole demo.
And, for some reason, instead of the default output which is formatted like a table, I want output presented like this.
123 .ps1 file extension: 11.xlsx file extension: 3.dll file extension: 1
This is a silly example, but notice that even though there are extensions of varying length (.ps1 and .dll are four characters including the dot, and .xlsx is five), all of the “file extension: <number>” is aligned.
How’d I do that?
Read on to learn how.