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.

This repository contains all the work done by me for Coursera's Deep Learning Specialization.

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

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.

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.

👾 Specialization on Deep Learning with TensorFlow on Coursera offered by DeepLearning.AI

Rnn (vanial, GRU and LSTM) from scratch

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

Master MVA - Parsimonious Representations Project

Predictes the posible full word based on given partial text.

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

Generated text using RNN(Gate Recurrent Unit: GRU).

Neural Networks project for Intelligent Systems course at Tecnico, Lisbon.

Implementation of Dynamic Memory Networks for QA System using Tensorflow