How I won my 48th bronze medal by using keras BiLSTM model on Kaggle’s disaster tweets

Because Flax is such a complex library, intended for machine learning research purposes, I decided to take a break from it and reintroduce myself to Keras Tennsorflow because it is easier to work with and has more documentation that I can prise from the internet.

Because I wanted to see how Keras Tensorflow fares in natural language processing, I decided to try out a BiLSTM model in Kaggle’s Disaster Tweet competition, which can be found here:- https://www.kaggle.com/competitions/nlp-getting-started

I decided to try out a BiLSTM model to see how it compares with the other models that I have used to solve the competition problem.

A recurrent neural network (RNN) is a type of artificial neural network that uses sequential data or time series data. These deep learning algorithms are commonly used for ordinal or temporal problems, such as language translation, natural language processing (nlp), speech recognition, and image captioning. RNNs are distinguished by their “memory” as they take information from prior inputs to influence the current input and output. While traditional deep neural networks assume that inputs and outputs are independent of each other, the output of recurrent neural networks depend on the prior elements within the sequence…