TensorFlow

A custom convolutional neural network (CNN) was developed from scratch and trained to recognize flower types using pcituires from the Flowers Recognition Dataset. The model’s architecture and hyperparameters were optimized to maximize recognition accuracy.

The custom CNN is compared against MobileNetV2, a pre-trained model fine-tuned using transfer learning. The comparison highlights the trade-offs between custom-built models and transfer learning approaches in terms of accuracy, training efficiency, and computational cost.

Practical tasks on Deep Learning (DL) and Neural Networks (NN). 🤖

This project leverages the Brain Tumor Image database to develop a semantic segmentation model for detecting brain tumors. The model is based on an encoder-decoder U-Net architecture, which classifies each pixel in a medical image as either tumor or healthy tissue. The resulting segmentation is visualized as a binary mask, clearly delineating the tumor region from the surrounding healthy areas.

The model successfully localizes the tumor’s global position with high accuracy but the precise shape and boundaries remain approximate (moderate Dice coefficient). Extending the training phase with additional epochs could refine the segmentation quality, leading to a higher Dice coefficient and more accurate tumor delineation.

This project focuses on binary sentiment classification of movie reviews from the IMDb Reviews dataset, using three distinct models: a multilayer perceptron, a Word2Vec-based model and a recurrent neural network (RNN). Each model is designed to classify reviews as positive or negative, leveraging advanced techniques in natural language processing. The word embeddings generated by each model are visualized using dimensionality reduction techniques (PCA, t-SNE), providing an intuitive representation of the semantic space.

Models are assessed using confusion matrices to analyze classification accuracy and ROC curves to evaluate the trade-off between true positive and false positive rates. The project compares the performance of MLP, Word2Vec, and RNN in capturing sentiment from text data.

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

A Python library for Secure and Explainable Machine Learning

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

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

Fundamental theory and practice in Data Science (DS). 🧮

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

web-interface

Old functions to play with neural networks. Developed in December 2024

ML model training

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.

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.

Android implementation of an offline computer vision algorithm for Malaria parasite detection and classification in thick blood smears (see research project).

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.

This repository is a testament to my journey of learning Tensor Flow from scratch (basics to intermediate level) using Google’s official Tensor-Flow documentation.

Tutorials from tensorflow, for learning purposes