A handbook from easy to superior frequency evaluation: exploring a significant instrument which is broadly underutilized in information science
Frequency evaluation is extraordinarily helpful in an enormous variety of domains. From audio, to mechanical methods, to pure language processing and unsupervised studying. For a lot of scientists and engineers it’s a significant instrument, however for a lot of information scientists and builders it’s hardly understood, if in any respect. In case you don’t learn about frequency evaluation, don’t fret, you simply discovered your handbook.
Picture by Daniel Warfield utilizing p5.js. All pictures on this doc are both created with p5.js or Python’s Matplotlib library except in any other case specified.
Who’s this convenient for? Anybody who works with just about any sign, sensor, picture, or AI/ML mannequin.
How superior is that this put up? This put up is accessible to newcomers and accommodates examples that may curiosity even essentially the most superior customers of frequency evaluation. You’ll probably get one thing out of this text no matter your ability stage.
What is going to you get from this put up? Each a conceptual and mathematical understanding of waves and frequencies, a sensible understanding of learn how to make use of these ideas in Python, some frequent use instances, and a few extra superior use instances.
Notice: That will help you skim by way of, I’ve labeled subsections as Fundamental, Intermediate, and Superior. This can be a lengthy article designed to get somebody from zero to hero. Nevertheless, if you have already got training or expertise within the frequency area, you’ll be able to most likely skim the intermediate sections or soar proper to the superior matters.
I’ve additionally arrange hyperlinks so you’ll be able to click on to navigate to and from the desk of contents
Click on the hyperlinks to navigate to particular sections
1) The Frequency Area
1.1) The Fundamentals of the Frequency Area (Fundamental)
1.2) The Specifics of the Frequency Area (Intermediate)
1.3) A Easy Instance in Python (Intermediate)
2) Widespread Makes use of of the Frequency Area
2.1) De-trending and Sign Processing (Intermediate)
2.2) Vibration Evaluation (Superior)
3) Superior Makes use of of the Frequency Area
3.1) Data Augmentation (Superior)
3.2) Embedding and Clustering (Superior)
3.3) Compression (Intermediate)
4) Conceptual Takeaways for Data Scientists