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.

SageMaker implementation of recurrent neural networks (RNNs) for time series forecasting.

Social media platforms like Twitter are key sources of information during disasters, but sorting relevant tweets is challenging. In this project, I used machine learning to classify tweets as disaster-related or not, applying models like RNNs, BiLSTMs, GRUs, and ALBERT on a Kaggle dataset labeled for disaster content.

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.

this is a project that predicts the cloud coverage percentage int next 15, 25 and 30 minutes based on the data of past 6 hours

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

fantasy football performance predictor

Developed an NLP system using Gradio and Hugging Face to classify disaster tweets with both machine learning (ML) and deep learning (DL) models.

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

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

Streamlit-based web app for classifying chest X-ray images to detect COVID-19 and Pneumonia using TensorFlow and Keras

Master MVA - Parsimonious Representations Project