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).
I decided to use my own domain instead of renting the /github.io/, and also to insert Google adsense in my blog if possible. Even if I updated my blog only 10 times since Oct, 2017, the number of visitors and their sessions were steady by Google analysis. I appreicate the interest on my posts. Recently I started updating my blog again, and want to see the more industrial analytic result. At least I am sure the profit from the adsense will cover the cost for the domain.
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.
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.
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.
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.
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.
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.
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.
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.