BiDAF is a popular machine learning model for Question and Answering tasks. This article explains the modeling layer of BiDAF with the help of some cute Minions. (By Meraldo Antonio) This article is the last … Read more
This article from last year popped up in my newsfeed recently. It contains a discussion on whether AI systems today display true intelligence. I would like to focus on the following quote from computer scientist … Read more
Sequence-to-sequence (seq2seq) and Bi-Directional Attention Flow (BiDAF) are influential NLP models. These models make use of a technique called “attention” that involves the comparison of two sequences. In this article, I explain how the attention mechanism … Read more
What is RPA? The buzzword “RPA” has been gaining traction in recent years across different industries, but what is it exactly and why is it so popular? I too, had a similar chain of thoughts … Read more
BiDAF is a popular machine learning model for Question and Answering tasks. This article illustrates how BiDAF uses three embedding mechanisms to convert words into their vector representations. (By Meraldo Antonio) This article is the second … Read more
This article illustrates the workings of BiDAF, an NLP model that has pushed the envelope in the Question and Answer domain. (By Meraldo Antonio) The year 2016 saw the publication of BiDAF by a team … Read more
A compiled visualisation of the common convolutional neural networks.
Recurrent neural networks are a class of artificial neural networks which are often used with sequential data. The 3 most common types of recurrent neural networks are vanilla recurrent neural network (RNN), long short-term memory … Read more
Gradient descent is an optimisation method for finding the minimum of a function. It is commonly used in deep learning models to update the weights of the neural network through backpropagation.
In this post, I will summarise the common gradient descent optimisation algorithms used in popular deep learning frameworks (e.g. TensorFlow, Keras, PyTorch, Caffe). The purpose of this post is to make it easy to read and digest (using consistent nomenclature) since there aren’t many of such summaries out there, and as a cheat sheet if you want to implement them from scratch.
A semi-supervised graph-based approach for text classification and inference In this article, I will walk you through the details of text-based Graph Convolutional Network (GCN) and its implementation using PyTorch and standard libraries. The text-based … Read more