Day: July 21, 2021

I finally got around to trying out a reinforcement learning exercise this weekend in an attempt to learn about the technique. One of the most interesting blog posts I read is Andrej Karpathy’s post on using reinforcement learning to play Pong on the Atari 2600. In it, Andrej uses the Gym package in Python to play the game.

This won’t be a post diving into the details of how reinforcement learning works; Andrej does that far better than I possibly could, so read the post. Instead, the purpose of this post is to provide a minor update to Andrej’s code to switch it from Python 2 to Python 3. In doing this, I went with the most convenient answer over a potentially better solution (e.g., switching `xrange()` to `range()` rather then re-working the code), but it does work. I also bumped up the learning rate a little bit to pick up the pace a bit.

Click through for the (slightly) updated code.

In Data Science projects, we distinguish between descriptive analytics and statistical models running in production. Overall, these can be seen as one process. You start with analyzing historical data to gain insights, find correlations, and finally develop and optimize your model. Then you transfer it and use it in your running system. A key point for every data scientist is not just the mathematical skills themselves, but also how to get the data into your analytics program.

In this blog post, we focus exactly on this crucial step: retrieving the data. In a second article, we’ll talk about running your model on real-time data.

Click through for the techniques.

Specifically, this can control the output when we embed a numeric value inside a string. Passing in special formatting instructions will make it easy to display values with commas, as currency, or even as hexidecimal.

For all of the examples, we’ll display the code, then under it the result of our code. In this article I’ll be using PowerShell Core, 7.1.3, and VSCode. The examples should work in PowerShell 5.1 in the PowerShell IDE, although they’ve not been tested there.

Robert has quite a few examples, so check them out.

I started writing this post 2-3 years back. Mainly when Apache Spark 2.3 started supporting Kubernetes (K8s) in 2018. It was obvious that Kubernetes is taking over app hosting space the same way virtual machines took over physical machines. All are expected to understand where the industry is moving and adopt. Hence I paused this post as there is nothing I need to endorse. But it’s time to resume this post and publish it.

Click through for a slew of thoughts on the topic.

You have the ability to run these on-premises (complex) or in a cloud service, like AWS or Azure. Hence AKS – Azure Kubernetes Service which helps reduce the complexity and operational overhead of managing Kubernetes by offloading much of that responsibility to Microsoft. You may be wondering how does containers relate to this? It was something on my mind when I first entered into this technology. Remember that containers is the next step beyond traditional virtualisation, you can run SQL Server Linux in containers, as an example. I then look at AKS as the “management” layer of the container solution, carrying out tasks such as scheduling, scaling, health, load balancing and host management.