Structural Engineer / Programmer

Blog Ritchie Vink

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Intro At the moment of writing this post it has been a few months since I’ve lost myself in the concept of machine learning. I have been using packages like TensorFlow, Keras and Scikit-learn to build a high conceptual understandig of the subject. I did understand intuitively what the backpropagation algorithm and the idea of minimizing costs does, but I hadn’t programmed it myself. Tensorflow is regarded as quite a low level machine learning package, but it still abstracts the backpropagation algorithm for you.

Recap Last post I described what my motivations were to start building a music classifier, or at least attempt to build one. The post also described how I collected a dataset, extracted important features and clustered the data based on their variance. You can read the previous post here. This post describes how I got my feet wet with classifying music. Spotify kind of sorted the data I’ve downloaded by genre.

Introduction As a nerd I am fascinated by the deep learning hype. Out of interest I have been following some courses, reading blogs and watched youtube video’s about the topic. Before diving into the content, I really thought this was something solely for the great internet companies and that it was not a subject us mortals could understand. While reading and learning more about it I’ve come to the insight that making use of deep learning techniques is not only something the internet giants and scientists can do.

In the last couple of weeks I have been playing with the results of the Fourier Transform and it has quite some interesting properties that initially were not clear to me. In this post I summarize the things I found interesting and the things I’ve learned about the Fourier Transform. Application The Fourier Transformation is applied in engineering to determine the dominant frequencies in a vibration signal. When the dominant frequency of a signal corresponds with the natural frequency of a structure, the occurring vibrations can get amplified due to resonance.