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.
I believe that semantics matter. I do realize not all semantics matter equally. But, still: semantics matter. It’s disappointing when otherwise intelligent people seem to dismiss something simply because they feel it’s just semantics. Let’s talk about this.
If you are going to have an AI that “does testing” — as opposed to some other activity like analysis or pattern recognition — you are going to have to move from a focus solely on perception and add the dimension of actions. I’m finding a lot of folks promising “AI-based testing tools” or those eagerly hoping for them are very much confusing this distinction. So let’s talk about it and, as we do, let’s create an illustrative example.
In this post I want to explore how a theory of constraints can be combined with cost of mistake curves to consider how testing operates, first and foremost, around the concept of design. Keeping design cheap at all times is a value of testing that I rarely see articulated. So here I’ll give that articulation a shot.
I get asked this a lot. I’ve been doing some form of testing since the early 1990s and while my initial opportunities were provided by chance, my career was one of choice. Rather than say why I stay in testing, I’ll frame this around some questions and answers that may give some insight of how testing has allowed me to answer certain questions in a career-relevant way.
My contention is that specialist testers know enough to not use the term “non-functional.” And if they are in environments where this term is used, they seek to shift people from this vocabulary. This is one of the ways that I spot specialist testers. Let’s talk about my rationale for this and why I think it’s important.
I’ve talked about interviewing testers before and I’ve talked specifically about hiring test specialists. Here I’m going to try to be a bit more concise, yet also a bit more expansive, about exactly what I think it means to look for specialist testers.
I periodically find myself questioning the extent to which it makes sense to blog. I find it’s healthy to go through these periods of reflection and introspection. I often find it’s even healthier to expose these thoughts to others.
I recently talked about a focus on being able to test an AI before you trust an AI to test for you. Here I want to provide a bit more focus on how worth it this idea might be. But my goal here is not to dampen the spirits of those who want to build such tools; rather I want to suggest some of the challenges and provide a bit of the vocabulary. I want to give you a way to frame the current situation with AI and its value as a test-supporting technology.
A lot of testers are talking about how to use artificial intelligence (AI) or machine learning (ML) to be the next biggest thing in the test tooling industry. Many are in what seem to be a lemming-like hurry to abdicate their responsibilities to algorithms that will do their thinking for them. Those same testers, however, often have absolutely no idea how to actually test such systems in the first place. So let’s dive into this a bit.