In the last post, we defined our neural network by providing it some specific hidden layers that will provide the basis for how the neural network model actually works. We were also able to dig in a bit to what’s happening behind the scenes. In this post, we’ll actually execute the neural network by feeding it the data and evaluating what gets produced as output.
In the previous post in this series we implemented a neural network from top to bottom, essentially allowing you to see how everything works from the start of inputting data to getting results. In this post, we’ll start to create a very similar neural network but I’ll take a bit more time to explain some of the specifics. Fair warning: this is probably going to be the longest post in this series so far because there’s a lot to dig into.
In the previous posts in this series, we got a lot of terminology placed in context, we investigated our data set, we took a dive into some math, and we talked about the life cycle of a neural network. In this post, you’re going to get a rapid-fire tour of creating a neural network from start to finish. The goal won’t be to understand every aspect, but rather just to get functionality working that I can expand on for you.
In the previous post in this series we were able to dive in a bit and get coding. That was a nice balance with the first post which put more emphasis on theory. In this post we’ll deal with some of what’s going on between the coding and the theory. This is where some of the practice comes in.
Here we’ll pick up from the first post and get to work with our machine learning, specifically focusing on getting a data set, exploring it a bit, and then doing some processing on the data to get it into a format that would be usable for a neural network algorithm.
This series will be part of my ongoing topic around machine learning. In these posts my goal is to allow you to deep dive into a common example for those first starting out in the subject. By the end of these posts you will have written and tested a neural network to solve a classification and prediction problem and come away with some understanding of a fast-growing trend in the industry.
This is the last of a four part series (see parts 1, 2 and 3). The goal has been investigating whether specialist testers have a role in machine learning environments uniquely distinct from development roles in those same environments. These posts have been getting you up to speed on what that might look like. Here we finish off that journey.
This post continues on directly from the first and second parts. I covered a lot of material in those posts so I can’t easily recap it here so definitely read those before reading this one. Here we’ll dig more into how a tester actually tests in this context but also look at testing as a framing activity.
This post continues on directly from the first one in the series. We’ll take the CartPole example we started with and continue our journey into how testing — particularly that done by a specialist tester — intersects with the domains of data science and machine learning.
As a tester are you ready to work in environments that are based in or around data science and machine learning? What will you actually do in these environments? How will you interact with developers? How technical do you have to be? Is it all just automated testing? Or do we still have room for a human in there somewhere? Let’s dig into this a little bit by going through a scenario.
This post continues on from the first part where I went over the high-level details of a tester getting involved in a machine learning context. I left off just at the point of introducing the algorithm and letting us get to work. So here, in this post, we’re going to dig right in.
I’ve talked before about the intersection of testing and AI as well as provided a series of posts, using a Pac-Man clone to further introduce testers into algorithmic searching. Here I’ll consider a really simple example of engaging with a machine learning example. I’ll focus on reinforcement learning, which often isn’t talked about as much.