ML models for Image captoining using CNN+LSTM and ResNet+GRU on the Flickr8k dataset

Sentiment Classifier using: Softmax-Regression, Feed-Forward Neural Network, Bidirectional stacked LSTM/GRU Recursive Neural Network, fine-tuning on BERT pre-trained model. Question Answering using BERT pre-trained model and fine-tuning it on various datasets (SQuAD, TriviaQA, NewsQ, Natural Questions, QuAC)

Modelos de RNN que auxiliarão a FEJESP na seleção de empresas juniores para o TransformeSP

Performed text categorization on 50K IMDB movie reviews using LSTM and GRU

Here I have created a Gated Recurrent Units based deep learning model which is capable of predicting and forecasting bitcoin prices with a Root Mean Squared Error of 1.7 and R2 Score of 0.98.

Established ML benchmark for 48-mortality prediction using MIMIC-III data and the FIDDLE Preprocessing Technique

Spam-filter application using RNN

This repository contains the complete tutorial with implementation of NLP and from scrach implementation of GRU and LSTM and RNN architectures in pytorch. Imbd data set used for sentiment analysis on each of these architectures. And also have the implementation of concepts like embeddings etc.

The study of negative online behavior, like toxic comments i.e. comments that are rude and disrespectful or otherwise are likely to make someone leave a conversation.

Homework for Graduate Deep Learning Course

Deep learning methods for sentiment analysis classification of covid-19 vaccination tweets

In the landscape of healthcare, data science is imperative in guiding pivotal public health decisions, optimizing resource allocation, and establishing tangible avenues. This project aims to contribute to these objectives by embarking on a comprehensive exploration of a dataset through data cleaning, preprocessing, and compelling visualizations.

Performance analysis of different Artificial Neural Networks

Predicting COVID-19 Cases Using Time Series Analysis with Neural Networks in Python. Video of the presentation: https://www.youtube.com/watch?v=XtAontZl-IE&feature=youtu.be

Our goal is to develop a sentiment classifier using a bidirectional stacked RNN with LSTM/GRU cells for twitter sentiment analysis.

Sentiment analysis lets you analyze the sentiment behind a given piece of text. In this notebook, we have done sentimental analysis for amazon shoe reviews, using 2 RNN models(LSTM and GRU)

The task of post modifier generation requires to automatically generate a post modifier phrase describing the target entity (an entity essentially refers to a noun but here we only consider people) that contextually fits in the input sentence.

Zebrafish project for Champalimaud Research