There are 3 repositories under lstm-neural-network topic.
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
PyTorch based autoencoder for sequential data
Neural Network that is able to translate any sign language into text.
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.
[PAMI 2021] Gating Revisited: Deep Multi-layer RNNs That Can Be Trained
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 ;)
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.
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.
Analysis Of The Context Size Impact In Deep Learning Conversational Systems
My work during the research internship "Automotive Control using Surface Electromyography" at The University of Tokyo, Jun.-Sep. 2020
This is a software application to detect network intrusion by monitoring a network or system for malicious activity and predicts whether it is Normal or Abnormal(attacked with intrusion classes like DOS/PROBE/R2L/U2R).
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"
RNN implementation of a voice activity detector to control Chromecast device volume.
✉️ 🐖 Spam email identification using NLP and a RNN with TensorFlow
SuperNova Artificial Inference by Lstm neural networks (SNAIL)
An image captioning system that is able to predict and speak out a caption of an image taken by visually impaired.
Using LSTM model to predict temperature using data of previous 3hours.
🤖 Predicting the stock price using LSTM (Deep Learning)
Caption Generation is a challenging artificial intelligence problem where a textual description must be generated for a given photograph.
Forecasting air pollution from given weather data. Two models were implemented: Regression Trees and LSTM model.
Prediction of Stock price using Recurrent Neural Network (RNN) models. Contains GRU, LSTM, Bidirection LSTM & LSTM combinations with GRU units. The models were deveoped using the keras module from Tensorlfow.
This repository contains the implementation of a recurrent neural network (LSTM from keras library) with the purpose of forecasting target time series, given the targets historical records and covariates. The project uses a toy data set, while focusing on the data transformation tasks (pandas dataframes to 3D numpy arrays required by recurrent networks) and on the hyperparameters tuning tasks, taking advantage of keras_tuner package.
A fast, effective and accurate algorithm for univariate time series forecasting