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 … Continue reading
Author Archives: Jeff Nyman
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 … Continue reading
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 … Continue reading
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 … Continue reading
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 … Continue reading
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 … Continue reading
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 … Continue reading
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, … Continue reading
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 … Continue reading
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 … Continue reading
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 … Continue reading
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 … Continue reading