kochlisGit/LSTM-Stock-Predictions

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kochlisGit/LSTM-Stock-Predictions

kochlisGit

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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.

ProphitBet-Soccer-Bets-Predictor

ProphitBet is a Machine Learning Soccer Bet prediction application. It analyzes the form of teams, computes match statistics and predicts the outcomes of a match using Advanced Machine Learning (ML) methods. The supported algorithms in this application are Neural Networks, Random Forests & Ensembl Models.

TraderNet-CRv2

TraderNet-CRv2 - Combining Deep Reinforcement Learning with Technical Analysis and Trend Monitoring on Cryptocurrency Markets

Jupyter Notebook

Unet3-Plus

Clean Implementation of Unet3+ and validation on Cityscapes dataset.

Autonomous-Vehicles-Adaptive-Cruise-Control

An implementation of an Autonomous Vehicle Agent in CARLA simulator, using TF-Agents

Physics-Informed-Neural-Network-PINN-Tensorflow

Implementation of a Physics Informed Neural Network (PINN) written in Tensorflow v2, which is capable of solving Partial Differential Equations.

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Advanced-ML

Advanced Machine Learning Algorithms including Cost-Sensitive Learning, Class Imbalances, Multi-Label Data, Multi-Instance Learning, Active Learning, Multi-Relational Data Mining, Interpretability in Python using Scikit-Learn.

Jupyter Notebook

Deep-RL-Frameworks

Comparison of different Deep Reinforcement Learning (DRL) Frameworks. This repository includes "tf-agents", "RLlib" and will soon support "acme" as well.

Jupyter Notebook

Deep-Trainer

Monitor Your Workout through a Webcam/IP Camera. No equipment is required, other than a camera and a laptop. This application could potentially replace a personal trainer, making it the idea app for workout.

Shadow-Hand-Controller

Construction of controllers for Shadow-Hand in Mujoco environment, using Deep Learning. 2 Different methods were used to create the controllers: a) Behavioral Cloning b) Deep Reinforcement Learning

Generative-Adversarial-Networks

Generation of Human-Like handwritten digits using different GAN Architectures. The models were developed using Low-Level Tensorflow.

LogoLens

Logo Detection of a custom small dataset. The dataset contains logos of 6 famous brands: Nike, Jordans, Adidas, Puma, Kappa, Quicksilver. The model was developed using the Object-Detection-API by Tensorflow

Jupyter Notebook

VIT2

This repository is the implementation of the paper: ViT2 - Pre-training Vision Transformers for Visual Times Series Forecasting. ViT2 is a framework designed to address generalization & transfer learning limitations of Time-Series-based forecasting models by encoding the time-series to images using GAF and a modified ViT architecture.

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Tensorflow-MNIST-State-Of-The-Art

Building High Performance Convolutional Neural Networks with TensorFlow

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Traffic-Sign-Classification

Apply built-in state-of-the-art classifiers with the Keras library to Traffic Sign datasets

Big-Data-Algorithms

Implementation of algorithms for big data using python, numpy, pandas.

Reinforcement-Learning-Algorithms

This project focuses on comparing different Reinforcement Learning Algorithms, including monte-carlo, q-learning, lambda q-learning epsilon-greedy variations, etc.

kochlisGit

This is my profile page.

Java-MultiThreading-Server-Chat

This is a simple Team Chat written in Java.

Data-Augmentation-Algorithms

Implementation of fast Data Augmentation for Image Classification / Detection tasks.

Data-Science-Algorithms

Implementation of statistics algorithms for Machine Learning & Data Mining. The algorithms were implemented with the Scikit-Learn Library

Predictive-Maintainance-Tanzania-Water-Pumps

In this project, I analyze, plot and clean Tanzania's Water Pump Dataset, which is provided by DrivenData.org for a competition.

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Noise-Adaptive-Driving-Assistance-System

Noise-Adaptive Driving Assistance System (NADAS) using Deep Reinforcement Learning, State-Estimation & State Representation

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Wine-Preference-Analysis

The purpose of this work is the modeling of the wine preferences by physicochemical properties. Such model is useful to support the oenologist wine tasting evaluations, improve and speed-up the wine production. A Neural Network was trained using Tensorflow, which was later tuned in order to achieve high-accuracy quality predictions.

Jupyter Notebook

Computer-Vision-Algorithms

Implementation of computer vision algorithms and image processing using Numpy & OpenCV

aboutme

In this repository, I will be hosting my personal webpage.

TMDB-Search-Engine

An implementation of an advanced movie search engine, using TMDB's data & Lucene's indexing. It is a desktop application, developed in Java