Pytorch

Introduction to Hamiltonian Monte Carlo One thing that has been occupying my head in the past couple of weeks has been HMC and how it can be used in large data/large model context. HMC stands for Hamiltonian Monte Carlo and it’s the de facto Bayesian method for sampling due to it’s speed. Before getting into big datasets and big models, let me motivate this problem a little bit. If you are new to Bayesian modelling, I have a little primer on the topic so I will assume for the most part you are familiar with basic Bayesianism. [Read More]

FizzBuzz is one of the most well-known interview questions. The problem is stated as: Write the numbers from 0 to n replacing any number divisible by 3 with Fizz, divisible by 5 by Buzz and divisible by both 3 and 5 by FizzBuzz. The example program should output 1, 2, Fizz, 3, Buzz, Fizz, 7, 8, Fizz, Buzz. A while back, there was this infamous post where the author claimed to solve this problem in an interview using tensorflow. [Read More]

There has been some really amazing advances in natural language processing (NLP) in the last couple of years. Back in November 2018, Google released https://ai.googleblog.com/2018/11/open-sourcing-bert-state-of-art-pre.html, which is based on attention mechanisms in Attention Is All You Need. In this two part series, I will assume you know nothing about NLP, have some understanding about neural networks, and take you from the start to end of understanding how transformers work. Natural language processing is the art of using machine learning techniques in processing language. [Read More]