Source code of CHAMELEON - A Deep Learning Meta-Architecture for News Recommender Systems

Smart Algorithms to predict buying and selling of stocks on the basis of Mutual Funds Analysis, Stock Trends Analysis and Prediction, Portfolio Risk Factor, Stock and Finance Market News Sentiment Analysis and Selling profit ratio. Project developed as a part of NSE-FutureTech-Hackathon 2018, Mumbai. Team : Semicolon

Predict hourly weather features given historical data for a specific location

Voice Activity Detection LSTM-RNN learning model

Time Series Analysis of Air Pollutants(PM2.5) using LSTM model

Neural Network that is able to translate any sign language into text.

PyTorch based autoencoder for sequential data

EA-LSTM: Evolutionary attention-based LSTM for time series prediction

I will be considering the google stocks data and will create a LSTM network for prediction.

Contains different course tutorials and jupyter notebook file for applying different Deep Learning models in different NLP tasks such as text classification, summarization, translation, etc.

This is Barclays PLC stock price prediction project using LSTM in Tensorflow Keras.

Long Short-Term Memory(LSTM) is a particular type of Recurrent Neural Network(RNN) that can retain important information over time using memory cells. This project includes understanding and implementing LSTM for traffic flow prediction along with the introduction of traffic flow prediction, Literature review, methodology, etc.

Find the origin of words in every language using a Deep Neural Network trained to create an etymological map.

This repo contains backtesting scripts for various models(mainly LSTM) using different type of datasets to predict bitcoin price. 
Upto 98.7% accuracy, but let me tell you it’s not enough to generate profits on a regular basis ;)

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

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

A Novel Approach leveraging Auto-Encoders, LSTM Networks and Maximum Entropy Principle for Video Super-Resolution (Upscaling and Frame Interpolation)

Building an LSTM Recurrent Neural Network for Predicting Stock Market Prices.

Training an LSTM network on the Penn Tree Bank (PTB) dataset

This is the official implementation of our research paper "One-day-ahead electricity load forecasting of non-residential buildings using a modified Transformer-BiLSTM adversarial domain adaptation forecaster"