The upcoming series of blog posts will contain my study of Rust standard library. I've partially written some parts of the series in scattered places and want to gather them in one place for better and easier access. I intend to update whenever I discover something interesting/important to remember. I'm referring to implementations in Rust … Continue reading Rust std study series: Vec
This series of posts is going to be a non-traditional intro to Rustlang, so buckle up! I will give more details when necessary. Codes are available in my github. Basic linear algebra such as vector inner product and General Matrix-Matrix (GEMM) product are at the heart of computer science so why not learning Rust by … Continue reading Learn Rust through linalg
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
My Summer internship at IBM as a Data Scientist has ended gracefully. I am very grateful for the opportunity. During the time, IBMers from various positions and background were asked to contribute filling the educational gaps exist in Data Science and Big Data by the team behind BDU and DSWB. Undoubtedly, BDU is becoming more … Continue reading Summer Internship at IBM as a Data Scientist
The idea of this post came up to my mind last night. I'm assuming you have already heard about the famous Monty Hall Problem (if you haven't, watch the quicker intro in Numberphile clip). Here I'd like to demonstrate a simulation taking the general case into account, i.e. assume we have $latex n$ bins (boxes or doors, … Continue reading General Monty Hall Simulation
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
In this post, I'll be summarizing the basics of the correspondence between vector bundles, locally free sheaves and divisors on a smooth curve (defined over an algebraically closed field $latex k$ of characteristic zero) together with some of their individual properties. Locally free sheaves and Vector bundles: Proposition 1: a) A coherent sheaf $latex \mathcal{E}$ on a … Continue reading Vector Bundles, Locally Free Sheaves and Divisors on a Curve