Every once in a while this topic comes up on a social media or Rust user channel. I'd like to describe briefly the way I see where things are going by a little bit of history as well as some information about existing flux of Machine Learning/Deep Learning frameworks and major recent trends. Brief history … Continue reading State of Machine Learning in Rust
Warning: This post contains a mixture of excitements, frustrations and rants! Today, Machine Learning/Deep Learning people have been sharing their great excitements over Ali Rahimi's talk at NIPS (from min 57 onwards). Undoubtedly, it's a great talk and you should check it out if you care about fundamental issues and the lost rigor in Deep Learning … Continue reading NIPS, AI hype and the lost rigor
In this post, I'm going to briefly write about the recently launched Andrew Ng's coursera courses in Neural Network and Deep Learning that I just finished with certificates. I want to argue that there's merit in taking these courses even if you're already familiar with some good portion of the syllabi. First a short relevant … Continue reading Overview of Andrew Ng’s deeplearning.ai courses
Word embedding is one of the interesting areas of research in Natural Language Processing. There are huge amount of materials with a lot of interesting ideas. I have been studying some of them lately and in this post, I'd like to create a brief account of the ideas I have found most interesting so far. … Continue reading What’s up with word embedding?
The idea of this post was sparkled from the new paper Developing Bug-Free Machine Learning Systems with Formal Mathematics. Meanwhile, I have had the idea of writing about what you're going to read for a long time and this paper happily forced me to do it finally! The first and final parts are about my journey and … Continue reading From Machine Learning to Formal Math and Type Theory
In this post, I'd like to show you how to use the newly written package on Bayesian Additive Regression Trees i.e. BART Machine for Restaurant Revenue Prediction with R. The datasets are part of the passed Kaggle competition that can be found here. What is BART? BART is the Bayesian sibling of Random Forest. For … Continue reading Restaurant Revenue Prediction with BART Machine