# Machine Learns from Cardiologist (4)

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)

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.

# Machine Learns from Cardiologist (2)

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)

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.

# Revisited Variational Inference

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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#### Namshik Kim

physicist, data scientist

Data Scientist