To architect low cost and well-performing server, many companies use cloud service such as Amazon AWS, Google clound platform (GCP). I have used AWS EC2 with GPU and S3 storage for my deep learning research at Soundcorset. AWS and GCP opened many cloud platform services, and to build the data pipeline and to manage the data effectively, need to learn the command line tool and API. In this post, I will discuss the Google Youtube data API because recently I studied.

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Today I want to discuss purely about coding itself. I wish this post is helpful for someone want to transit his career from a pure researcher to a programmer. I have been a researcher rather than a programmer. I would just want to execute something to see the result I wanted to see. If the run time is too long or my computer has no enough memory to run the code, it was a sign of new purchase to me.

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A few days ago, I was asked what the variational method is, and I found my previous post, Variational Method for Optimization, barely explain some basic of variational method. Thus, I would do it in this post. Data concerned in machine learning are ruled by physics of informations. It sounds quite abstract, so I will present an example of dynamic mechanics. Let us consider a ball thrown with velocity v=($v_x$, $v_y$) at x = (x, y), and under the vertical gravity with constant g.

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Around a week ago, on ArXiv, an interesting research paper appeared, which is about the music style transfer using GAN, which is also my main topic for recent few months. Around a week ago, on arXiv, an interesting research paper appeared, which can be applied to the music style transfer using GAN, which is also my main topic for recent few months. There are already many researches on the style transfer of the images, and one of my main projects now is making the style transfer in music.

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I want to introduce some GAN model I have studied after I started working for the digital signal process. I will skip technical detail of the introduction. My goal is to provide a minimal background information. Revolution in deep learning As we have seen at the post of VAE, generative model can be useful in machine learning. Not only one can classify the data but also can generate new data we do not have.

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Author's picture

Namshik Kim

physicist, data scientist

Data Scientist

Vancouver, BC, Canada.