Contains Deep Learning Content and Algorithm. ANN_CNN_RNN(LSTM-GRU)_AUTOENCODER

This project demonstrates how to build an image captioning model using TensorFlow. The model combines a pre-trained Convolutional Neural Network (CNN) for image feature extraction and a Long Short-Term Memory (LSTM) network for generating captions.

The goal of this project is to predict the Remaining Useful Life (RUL) of aircraft engines based on sensor data. Predictive maintenance helps identify the point at which an engine is likely to fail, allowing for timely maintenance to prevent failures and optimize maintenance schedules.

a project for predicting Google stock prices using deep learning techniques. The project involves data preprocessing, training a Long Short-Term Memory (LSTM) model, and visualizing the predictions against actual stock prices.

An autoregressive forecasting implementation of a LSTM network, NBEATS architecture and Autoformer architecture on rupee dollar exchange rates using pytorch, pytorch lightning, pytorch-forecasting, and GluonTS

Deep convolutional and LSTM feature extraction approach with 784 features.

Performance analysis of different Artificial Neural Networks

The primary objective of this project is to develop a cutting-edge forecasting model utilizing advanced machine-learning algorithms and sophisticated time-series analysis techniques. The model aims to deliver precise predictions of future sales across diverse retail outlets.

Crypto & Stock* price prediction with regression models.

Multivariate stock price forecasting

Thesis of my masters in Data Science. This project implements a deep learning framework applied to stock portfolio management. Using the top 20 stocks of FTSE (Financial Times Stock Exchange) top 100 by market share.

A deep learning project for automated chorus detection in songs, featuring a command-line interface (CLI) tool that allows users to input a YouTube link and utilize a pre-trained CRNN model to detect chorus sections from a song on YouTube

LSTM-ARIMA with Attention and multiplicative decomposition for sophisticated stock forecasting.

Predicting popular cryptocurrency prices with LSTM and other analyses on stock and crypto prices

Forecasting Customer Retention Trends by analyzing historical patterns and identifying key indicators of customer loyalty or churn.

Sipaling is a web application that predicts stock prices using an LSTM deep learning model

Neural network to analyze prices of stock trades in accordance to news articles. Read news without ads, and get up to date stock prices!

This repository introduces LSTM and its application in predicting stock prices.

Persian text simplification using lexical simplification

CS 182 Spring 2019 - Assignment 2