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PredictBay aims to revolutionize decision-making in investment strategies through intelligent forecasting. Our platform utilizes advanced machine learning algorithms to provide accurate predictions for stocks .

The goal of this small project was to see how easy or difficult it is to predict a signal made up of a significant amount of periodic components.

This is a repository with the assignments of IE678 Deep Learning course at University of Mannheim.

Word Embedding + LSTM + FC

RNN and CNN based Relation Extraction models in tensorflow 2.0

This repository contains code and documentation for a machine learning project focused on predictive maintenance in industrial machinery. The project explores the development of a comprehensive predictive maintenance system using various machine learning techniques.

decompilation and static-analysis on the prevalent hermeticwiper

Tools for machine learning from two courses machine learning specialization and deep learning specialization

This is a crowd forcasting project for Tourism Bureau of Tainan City Government, Taiwan. Based on mobile signaling, tickets, and open data, LSTM and GRU models were utilized to estimate people's flow in tourist attractions in Tainan City for traffic control or crowd flow measures.

Predicting PM10 air particles using recurrent neural networks (RNN, GRU, LSTM)

PredictBay is an innovative project that aims to revolutionize decision-making in investment strategies through intelligent forecasting. Our platform utilizes advanced machine learning algorithms to provide accurate predictions for stocks .

This is a learning notebook for RNN, LSTM and GRU

Challenging Memory-based Deep Reinforcement Learning Agents

Analysis and forecasting of Ukraine and neighboring countries' aviation traffic during the russian invasion.

PyTorch implementation of a sequence-to-sequence RNN model

This repository contains the code for the project based on Adversarial Neural Cryptography implemented in PyTorch framework as part of the SMAI course offered at IIIT Hyderabad (Spring 2023)