If you’re anything like me, you spent the first several months looking at applications of machine learning and wondering how to get better performance out of the model. I would spend hours, if not days, making minor tweaks to the model, hoping for better performance. Surely, I thought, there should be a better way to improve the model than manually checking dozens of combinations of hyperparameters.

That’s when I came across this excellent article on the Python package Hyperopt, which uses a Bayesian optimization model to determine the optimal hyperparameters for a machine learning model. Gone are the days of…

Here we are in the last section of this fun project! Here’s where we’ll see some practical applications of using the LSTM to emulate the PID controller, as well as some potential shortcomings. If you haven’t read the previous articles in the series, I highly recommend going back so you can have some context. And of course, the neat thing about this project is that you can run all the code on your own Temperature Control Lab device, simulating something that you might see in a real refinery or chemical plant. …

Welcome to part 3 of this project! By now, your employer is looking at your results and saying “so what?” You’ve done a lot of legwork in getting things going, but so far it just looks nice on a computer. Well, here’s the “so what” part. Here’s where we finally get to implement this LSTM neural network to emulate the behavior of the PID controller. As a quick recap, here’s where we’ve been and where we’re going:

Welcome to Part 2 of this exciting project! The results have looked great so far, and now we can get into the meat of what we’re trying to accomplish: emulating the behavior of a PID controller with an LSTM. As a recap, here’s what we’ve explored so far and where we’re going:

- Using the Temperature Control Lab to create Proportional-Integral-Derivative controller data
- Training a Long Short-term Memory neural network in Keras to emulate a PID controller (this article)
- Controlling the Temperature Control Lab with an LSTM
- Practical applications of temperature control using LSTM controller instead of PID controller

Do you ever just get really excited about an idea? Maybe it’s a new DIY project you’re tackling, or a cool assignment at work. Maybe you’re crazy like me and want to hike the Pacific Crest Trail (as I’m moving to Seattle soon, so I can’t help but get excited about the idea of flying down to San Diego and walking home). Well, this project is one of those types of ideas for me, and I hope you enjoy the ride!

Before I get started, though, I want to warn you that this is quite an extensive project, and so…

Anomaly detection is a powerful application of machine learning in a real-world situation. From detecting fraudulent transactions to forecasting component failure, we can train a machine learning model to determine when something out of the ordinary is occurring.

When it comes to machine learning, I’m a huge advocate for learning by experiment. The actual math behind machine learning models can be a bit of a black box, but that doesn’t keep it from being useful; in fact, I feel like that’s one of the advantages of machine learning. You can apply the same algorithms to solving a whole gamut of…

Merging the worlds of engineering and machine learning. LinkedIn: https://www.linkedin.com/in/nicholas-lewis-0366146b/