ML

Machine Learns from Cardiologist (4)

Nov 11, 2019

Update I had two emails about my ECG classifier Github repo from graduate students after I opened the source code. Please use the issue page of the repo if you have any question or an error of the code. I myself found some errors due to the version change of Python libraries, so I updated the codes. In the near future, I would update the Python codes suitable for upgraded libraries (won’t be posted).

Machine Learns from Cardiologist (3)

Mar 3, 2019

Open source The codes can be found at my Github repo. If you are familar to the models already, just see the codes. The codes are made from understanding of the research papers in Nature and the other and the open source. The host and main contributors of the linked repo are the co-authors of the original research papers. The two related research papers are easy to understand. If you do not have much time to read it, see their blog post about this research.

Machine Learns from Cardiologist (2)

Mar 3, 2019

Understand literatures and the result-analysis Deep learning and classifications. The pattern recognition using deep convolutional neural network is indisputably good. It shows in various complicated image recognitions or even sound recognition. It is obvious it is going to be so good at least as the similar level of human being. What matters is if we have enough data, and how we can preprocess the data properly for machine to learn effectively.

Macnine Learns from Cardiologist (1)

Mar 3, 2019

Prologue Recenly the interest on wearing device is increasing, and the convolutional neural network (CNN) supervised learning must be one strong tool to analyse the signal of the body and predict the heart disease of our body. When I scanned a few reseach papers, the 1 dimensional signal and the regular pattern of the heart beat reminds me of musical signals I researched in that it requires a signal process and neural network, and it has much potential to bring healthier life to humar races1, so I want to present the introductory post.

Youtube Data API on GCP

Feb 2, 2019

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.

Clean Coding and Short Run Time

Feb 2, 2019

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.

Revisited Variational Inference

Feb 2, 2018

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.

Rough Review of WaveGAN

Feb 2, 2018

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.

Introduction to GAN

Dec 12, 2017

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.

How to Test Progressive Growing of GAN from the Github Source

Dec 12, 2017

NVIDIA research team published a paper, Progressive Growing of GANs for Improved Quality, Stability, and Variation, and the source code on Github a month ago. I went through some trials and errors to run the codes properly, so I want to make it easier to you. Why I think this post will be helpful is the Github page is not supporting to post issues to ask and answer for inquiries.

Machine Learns from Cardiologist (4)

Machine Learns from Cardiologist (3)

Machine Learns from Cardiologist (2)

Macnine Learns from Cardiologist (1)

Revisited Variational Inference

Namshik Kim

Machine Learns from Cardiologist (4)

Machine Learns from Cardiologist (3)

Machine Learns from Cardiologist (2)

Macnine Learns from Cardiologist (1)

Youtube Data API on GCP

Clean Coding and Short Run Time

Revisited Variational Inference

Rough Review of WaveGAN

Introduction to GAN

How to Test Progressive Growing of GAN from the Github Source