Question answering is a very popular natural language understanding task. It has applications in a wide variety of fields such as dialog interfaces, chatbots, and various information retrieval systems. Answering questions using knowledge graphs adds a new dimension to these fields. “Question answering over knowledge graphs (KGQA) aims to provide the users with an interface… Continue reading Introduction to Question Answering over Knowledge Graphs
Category: Neural Networks
A Disciplined Approach to Neural Network Hyper-Parameters – Paper Dissected
Training a neural network requires carefully selecting hyper-parameters. The optimal parameters vary from one dataset to another. With so many things to tune, this can easily go out of control. Leslie N. Smith in his paper - A Disciplined Approach to Neural Network Hyper-Parameters: Part 1 - Learning Rate, Batch Size, Momentum, and Weight Decay discusses several efficient… Continue reading A Disciplined Approach to Neural Network Hyper-Parameters – Paper Dissected
What makes the AWD-LSTM great?
The AWD-LSTM has been dominating the state-of-the-art language modeling. All the top research papers on word-level models incorporate AWD-LSTMs. And it has shown great results on character-level models as well (Source). In this blog post, I go through the research paper - Regularizing and Optimizing LSTM Language Models that introduced the AWD-LSTM and try to explain… Continue reading What makes the AWD-LSTM great?
A Walkthrough of InferSent – Supervised Learning of Sentence Embeddings
Universal Embeddings of text data have been widely used in natural language processing. It involves encoding words or sentences into fixed length numeric vectors which are pre-trained on a large text corpus and can be used to improve the performance of other NLP tasks (like classification, translation). While word embeddings have been massively popular and… Continue reading A Walkthrough of InferSent – Supervised Learning of Sentence Embeddings
A Neural Network in PyTorch for Tabular Data with Categorical Embeddings
PyTorch is a promising python library for deep learning. I have been learning it for the past few weeks. I am amused by its ease of use and flexibility. In this blog post, I will go through a feed-forward neural network for tabular data that uses embeddings for categorical variables. If you want to understand the… Continue reading A Neural Network in PyTorch for Tabular Data with Categorical Embeddings
Understanding the Working of Universal Language Model Fine Tuning (ULMFiT)
(Edit) A big thanks to Jeremy Howard for the shout-out 😊 https://twitter.com/jeremyphoward/status/1008156649788325889 Transfer Learning in natural language processing is an area that had not been explored with great success. But, last month (May 2018), Jeremy Howard and Sebastian Ruder came up with the paper - Universal Language Model Fine-tuning for Text Classification which explores the benefits… Continue reading Understanding the Working of Universal Language Model Fine Tuning (ULMFiT)
Hotdog or Not Hotdog – Image Classification in Python using fastai
Earlier, I was of the opinion that getting computers to recognize images requires - huge amount of data, carefully experimented neural network architectures and lots of coding. But, after taking the deep learning course - fast.ai, I found out that it is not always true. We can achieve a lot by writing just a few lines… Continue reading Hotdog or Not Hotdog – Image Classification in Python using fastai
Which activation function to use in neural networks?
Activation functions are an integral component in neural networks. There are a number of common activation functions. Due to which it often gets confusing as to which one is best suited for a particular task. In this blog post I will talk about, Why do we need activation functions in neural networks? Output layer activation… Continue reading Which activation function to use in neural networks?