Hands-on Generative Adversarial Networks (GAN) for Signal Processing, with Python

In my research, I use Machine (Deep) Learning a lot. Two days ago, I was working on Generative Adversarial Network (GAN) and seeing how I can apply it to my work. After the code was ready, I started writing this article on  <a href="https://medium.com/u/504c7870fdb6?source=post_page-----ff5b8d78bd28--------------------------------" rel="noopener" target="_blank">Medium</a>  and I tried to find the best words to start with a proper introduction, as I always do. I start asking myself questions like: <blockquote> “Why should a reader read this? Why what I am trying to communicate is meaningful? What is the framework that we need to have before reading this?” </blockquote> Now, of course, I believe that the reader should read this because I think what I write is meaningful and interesting. But the truth is that I love signal processing, and I love writing about it because I love signal processing. This article is about the two things I love the most: signal processing and artificial intelligence. I put all my love, energy, and passion into these two (I actually crossed an ocean to research them) and I hope that you will find this topic interesting. <a href="https://towardsdatascience.com/hands-on-generative-adversarial-networks-gan-for-signal-processing-with-python-ff5b8d78bd28">Learn More</a>