🎬 Analyze movie reviews using LSTM for accurate sentiment classification, helping you understand viewer opinions with a balanced dataset.

Many-to-one LSTM neural network for binary sentiment classification of IMDB movie reviews. Built with TensorFlow/Keras as part of Deep Learning coursework. Includes data preprocessing, model training, evaluation, and visualization.

Machine learning pipeline for training ARIMA and LSTM models to forecast daily market prices of food products in Ethiopia. Powers the Bazarya price alert system.

Exploration of different models that generate text by predicting the next character.

🌾 Bazarya is a lightweight Flask-based web app that uses time-series forecasting to predict daily market prices of essential goods in Ethiopia. Built for low-resource environments to empower farmers, traders, and households with data-driven insights.

Tools and templates for organizing research and experiment workflows.

Autoencoders for vision and NLP tasks. Vision autoencoders use fully connected and convolutional architectures with layer-inverse constraints. NLP autoencoder employs LSTM-based sequence-to-sequence model for text denoising.

Time series forecasting project to to predict daily PM2.5 pollutant concentration levels in Tlaquepaque, Jalisco using LSTM.

Music generation using a Long Short-Term Memory (LSTM) neural network. The gennhausser project uses TensorFlow and music21 libraries to create a synthetic dataset, train an LSTM model, and generate music sequences.

This repository contains a project aimed at predicting Tesla's stock prices using Long Short-Term Memory (LSTM) networks.

AI-powered time-series forecasting system that predicts CPU, Memory, Network, and Disk utilization in cloud environments using LTSM neural networks.

Specialized LSTM & AI models

RNN, GRU, LSTM implementation using PyTorch and Numpy

Repo for data concerning a study comparing community impact on LITECOIN and DOGECOIN economical parameters

A computer vision model for Indian Sign Language Recognition

🚀 Unveiling Stock Market Insights with RNNs: A concise exploration of LSTM and GRU models for stock price prediction, featuring a research paper and Jupyter Notebook. 💹📈

The goal of the project is to predict chickenpox cases one year ahead based on known history. Methods used: ETS decomposition and SARIMA with statsmodels, LSTM with Keras, MINMAX scaling.

Contains ML projects