Python package for Granger causality test with nonlinear forecasting methods.

AI algorithm that plays Texas hold 'em poker (part of university research in imperfect information games)

[PAMI 2021] Gating Revisited: Deep Multi-layer RNNs That Can Be Trained

SANNet Neural Network Framework

Neural Persian Poet: A sequence-to-sequence model for composing Persian poetry

Speech recognition model for recognising Macedonian spoken language.

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.

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)

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

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

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)

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

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

Zebrafish project for Champalimaud Research

Predicting solar flares with simple RNNs, LSTMs and GRUs.

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.

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

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.