Challenge

I am working on a comparative analysis of Kubeflow (released 2018, Google, current version 0.7) and Metaflow (released December 2019, Netflix). At a high lev...

In this blog post, I’d like to share some of the insights from my work at the High-Performance Computing (HPC) Texas Advanced Computing Center (TACC) cluster...

The goal of this post is to compare NLP approaches for encoding words: (1) bag of words (2) tfidf (3) google GloVe emeddings Training set is a set of tagged...

I will explain the most recent trends in Machine Learning Automation as a Flow. Examples of ML Flow include Kubeflow by Google, MLFlow, MetaFlow by Netflix. ...

Supervised ML is a powerful tool, however there is a range of problems where its utilization can't be justified. Such problems include reasoning, inference -...

https://github.com/romanonly/romankazinnik_blog/tree/master/CVision/CNN Perhaps the greatest about CNN (Convolutional Neural Networks) is that CNN allows int...

Deep Learning is driven by the possibility of computing at-scale gradient of the minimization problem for Directed Acyclic Graphs (DAG). This kind of optimiz...

Here is a video of my talk at the University of North Carolina at Charlotte (01.12.2018). I show how at the beginning of AI, Deep Learning networks departed...

Trial and error reveal surprising patterns of success—and failure—using different BL and Supervised ML models. When we consider ML (Machine Learning) the imp...

In the field of data quality and predictive modeling, which modeling metrics are best at “telling the truth,” so to speak? How did I come to this question? I...