intercept <- 3 betas <- c(weight = 2, height = 4)
Our goal is to build a linear regression model that has the above coefficients. The way we are going to do this is by building our own synthetic data set such that the regression fit through this data set yields these coefficients.
It’s fairly straightforward to do this for linear models; as things get more complicated, however, the difficulty level spikes.
Let’s quickly review neural networks.
Neural networks are universal approximators. This means that with enough neurons and time, a neural network can model any input/output relationship, to any degree of precision.
A standard feed forward neural network receives an input (vector) and feeds it forward through hidden layers to an output. SAS PROC NNET, for example, trains a multilayer perceptron neural network. As the name “multilayer” implies, there are multiple layers. Below we see the inputs (features), one hidden layer and the output (response, target). Each neuron is simply a mathematical function.
This is a complicated topic explained well. It’s also an overview more than a tutorial.
We love xp_fixeddrives here, it’s a quick and simple way to see how much space you’ve got available on your drives. But there are just a couple of things that I really wish it would do better.
Firstly, I’d love to see the total size of the drive and possibly even a percentage of free space left.
Secondly, mount points. If you’ve got any databases that are on mount points, it’s not going to give you any idea of what you’ve got left on there.
This is the reason that I put together sp_drivespace.
Click through for the script. It would be interesting to see if this works on Linux as well.
The post references this guide to the machine learning services in Azure, along with their supported languages. Services that currently support R include Azure Machine Learning Studio, SQL Server Microsoft Machine Learning Service, Microsoft Machine Learning Server, Azure Data Science Virtual Machine, Azure Databricks, and more.
The R and Python programming languages are primary citizens for data science on the Azure AI Platform. These are the most common languages for performing data preparation, transformation, training and operationalization of machine learning models; the core components for one’s digital transformation leveraging AI. Yet they are fundamentally different in many aspects, directly affecting not only deployed solutions IT architectures but also but also corporate strategies for developer skills and product supportability.
This series of articles is designed help you understand the options your company and customers have to support and evolve their R strategy.
It’s good to see some of this out in the open for planning purposes.
KubeInvaders allows you to play Space Invaders in order to kill pods in Kubernetes and watch new pods be created (this actually might be my favourite github repo of all time).
I demo SQL Server running in Kubernetes a lot so really wanted to get this working in my Azure Kubernetes Service cluster. Here’s how you get this up and running.
I got to see Andrew show it off at SQL Saturday Cork and it was as fun as you’d expect.
This recipe primarily involves Power BI report design techniques. I’m not going to get into the details of Power BI report design but will cover the basics with a partially-completed report to get you started. If you are less-experienced with Power BI you can use this as an example for future report projects.
The sample database and files will be available in the forthcoming book: SQL Server Reporting Services Paginated Report Recipes, 2nd Edition (working title).
These instructions are provided as an example but refer to files that will be available when the book is published. Please contact me in the comments with questions and feedback.
You can’t get the files just yet, but you can see what Paul does to get this working.
When you’re dealing with a beast like DAX you can use any help there is, right? So here I show you how you can debug DAX variables who contain tables or show the result of multiple variables at once. So you can easily compare them with each other to spot the reason for problems fast.
Please note, that currently only comma separated DAX code is supported.
Click through for a demo as well as a video.
SQL Server 2019 is still in preview as I write this, but I wanted to point out a new feature that Microsoft has added to SQL Server Setup, on the Windows version.
On the Database Engine Configuration screen are two new tabs, called MaxDOP and Memory. These are both new configuration options for SQL Server 2019. This post will specifically look at the MaxDOP tab, and we’ll look at Memorynext week.
I like that they’re adding these things to initial setup; that makes it easier for people to remember that yeah, MAXDOP is important.