Estimating the growth or depreciation on exchange rates by using sentiment analysis method from social media comments

RNN is one of the very powerful deep-learning algorithm which works amazingly well on Sequential Data. As historical or past data plays major role in the prediction of sequential data, RNN takes these inputs of not only recent output but also past output. Here I have used GRU for the prediction of eminem's Rap.

With an ever-increasing amount of astronomical data being collected, manual classification has become obsolete; and machine learning is the only way forward. Keeping this in mind, the LSST Team hosted the PLAsTiCC in 2018. This repository details our approach to this problem.

It analyses the movie review entered by a user for any specific movie and analyses what is the sentiment of the review. It helps the companies rate the movie and understand crowd sentiment regarding it. Sentiment analysis is a natural language processing problem where text is understood and the underlying intent is predicted.

🔁Graphical models, Recurrent Neural Networks and SIFT algorithm for image processing, signal analysis and timeseries forecasting (MD Course: Intelligent Systems for Pattern Recognition)

doctor_prescription_recognization_using_DeepLearning project for epics

We use machine learning tools to predict the price of ethereum from historical data, economic indicators, and community sentiment on ethereum specifically from twitter.

👨🏻‍💻 My own repository to explore LearnQuran tech product in particular -obviously- AI stuffs

This repository contains three variants of a Sentiment Analysis model that uses a GRU (Gated Recurrent Unit) to predict the sentiment of a given text as either positive or negative. The models were built using PyTorch, and the training and testing data came from DLStudio

Developing a PyTorch-based solution for predicting future values in financial time series data, leveraging RNNs and GRUs as part of the M3 competition for time series forecasting.

Image captioning with a benchmark of CNN-based encoder and GRU-based inject-type (init-inject, pre-inject, par-inject) and merge decoder architectures

Relationship Extraction using a Bi-directional GRU v/s CNN with multiple layers and max-pooling

GRU DRNN model that generates classical music.

Time Series Forecasting with Neural Networks

Details of certified courses covered by me. Includes notes and solutions to programming exercises.

An implementation of classical GRU (Cho, el at. 2014) along with Optimized versions (Dey, Rahul. 2017) on TensorFlow that outperforms Native tf.keras.layers.GRU(units) implementation of Keras.

The model helps in predicting toxicity of Online comments, trained on Wikipedia comments data using Deep Neural Network (GRU+ GLoVe ))

To tackle the spread of toxicity on internet that many times leads to depression & loss of confidence I have built a deep learning system that determines whether a text contains toxicity or not.