TensorFlow

Trading Bot – Algorithmic Crypto Trading with AI Integration

This project is a powerful algorithmic trading bot for cryptocurrency markets. It combines traditional technical analysis with modern machine learning to generate accurate and intelligent trading decisions.

Key Features:

Candlestick Pattern Detection: Identifies classic reversal patterns such as Hammer, Doji, Engulfing, Shooting Star, and complex formations like triangle patterns.

Technical Indicators: Includes standard indicators (RSI, MACD, Moving Averages, Bollinger Bands) and advanced tools like Ichimoku Clouds, SuperTrend, Fibonacci Retracements, and more.

Machine Learning Integration: Uses LSTM-based models for time-series forecasting and momentum strategies, combined with indicator signals through weighted evaluation.

Dynamic Signal Weighting: Customizable signal weighting for patterns, indicators, and ML predictions with automatic adjustments to market volatility.

Trade Execution Engine: Supports long/short positions with stop-loss, take-profit, and trailing stop features. Automatically includes fees and tax deductions in profit calculations.

Backtesting & Debugging: Simulates strategies on historical data with detailed equity/value curve visualization and comprehensive debug logs.

Robust Error Handling: Detects and logs data inconsistencies, index errors, and processing issues to ensure stability.

Modular architecture with key components such as TraderBot, SignalHandler, PatternManager, IndicatorManager, MLModelHandler, SequenceManager, DataAPI, and CryptoCurrency. Additional support provided by PatternCalculator, IndicatorCalculator, and DataProcessing.

Version: V1.3.0.0 | GUI: V1.0.0 Author: Marian Seeger – info@seegersoftwaredevelopment.de

A Computer Vision algorithm for Malaria parasite detection and classification in digital images of thick blood smears.

A project that utilizes machine learning to predict shrimp growth to optimize the amount of feed used.

A TensorFlow/Keras neural network for regression on noisy sine wave data, predicting continuous values with real-time visualization of predictions and loss using Matplotlib.

Introduction to classification using machine learning and deep learning (PyTorch, TensorFlow, Keras)

Tutorials from tensorflow, for learning purposes

Cyber determetion toolskit

This project provides a deep learning approach to learn machining features from CAD models using a hierarchical graph convolutional neural network.

This project focuses on building a Lyric Classification Model using LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Networks). The models' purpose is to identify which artist a given lyric belongs to. Users can input lyrics and the models will predict the associated artist, aligning with the course's focus on the practical implementation of Natural Language Processing (NLP) tasks.

The project focuses on developing a predictive tool for chess using recurrent neural network (RNN) models implemented in TensorFlow. Data collection was carried out using web scraping techniques from the website https://www.chess-poster.com.

El proyecto se orienta hacia el desarrollo de una herramienta predictiva para el ajedrez, empleando modelos de red neuronal recurrente (RNN) implementados en TensorFlow. La recopilación de datos se realizó mediante técnicas de web scraping desde la página https://www.chess-poster.com.

An example Python project for simulating federated learning using federated averaging.

A Python library for Secure and Explainable Machine Learning

Documentation available @ https://secml.gitlab.io

Follow us on Twitter @ https://twitter.com/secml_py

MediScanAI is a computer vision healthcare API project applying AI for medical insights. It classifies brain tumor types based on tomography images.

ICT deep learning lab

This Car Prediction Project aims to predict v price, using Regression models. The project contains a collection of data files, model files, and Python scripts necessary for training and deploying car prediction Price models. This project encompasses a comprehensive set of data files, serialized models, and Python scripts necessary for training, evaluating, and deploying car price prediction models.

An attempt at AI-based lemmatization (or something close to lemmatization) of Old English

Transform text into lifelike speech with advanced neural network models

Prediction of age from X-Ray images of hand bones using deep learning models. We used 3 models: Shallow, ResNet50, and InceptionV4. The best result achieved was with a mean absolute error of 10 months using InceptionV4. The preprocessing of data included computer vision techniques like CLAHE filter and reducing channels, and also creativities such as using the Google MediaPipe library to detect hands and crop on them.

Рабочий сервис генерации продолжения текста песни по короткой фразе на основе текстов песен группы Metallica