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🔍 A repository showcasing the application of genetic algorithms to Conway's Game of Life, exploring various configurations and optimization strategies.
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🧬 Implements genetic algorithms for evolving initial configurations, resulting in diverse and stable patterns over generations.
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📈 Insights into algorithm performance and behavior, including optimal parameters and notable patterns observed during execution.
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🛠️ Utilizes C++ for efficient simulation and exploration of cellular automata behavior.
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🏷️ GeneticAlgorithms , Optimization , EvolutionaryAlgorithm , Gtk3 , CSS
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🔍 Advanced AI Techniques: Employs constraint satisfaction and backtracking algorithms, alongside AI strategies, to efficiently solve Sudoku puzzles of any difficulty.
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🤖 Interactive GUI Experience: Features an interactive GTK5 GUI, allowing users to seamlessly input puzzles, visualize the solving process in real time, and engage with the solver in an intuitive manner.
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🚀 C++ and Qt Integration: Demonstrates the power of C++ for core algorithm implementation, enhanced with a modern Qt interface for a user-friendly experience.
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🎓 Educational Insight: Provides a unique opportunity to explore advanced algorithmic and AI concepts through the lens of Sudoku solving, making it an excellent tool for learning and experimentation.
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📝 Open Source Collaboration: Encourages contributions and is designed as an open-source project to foster community involvement and continuous improvement.
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🏷️ ConstraintSatisfaction , CSP , AI , Qt5 , Propagation , AC-3
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🔍 An interactive GUI for visualizing and experimenting with Hopfield networks, a type of recurrent neural network used for associative memory.
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📊 Features real-time manipulation of neuron states, dynamic visualization of network states, and the ability to store and retrieve patterns.
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📚 Incorporates a range of tools and functionalities like pattern addition, state reset, energy calculation, and more, providing a comprehensive understanding of Hopfield networks.
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🧬 Designed to offer educational insights into neural network dynamics, allowing users to explore concepts such as energy landscapes, convergence, and pattern stability.
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🏷️ HopfieldNetwork , NeuralNetworks , Matplotlib , NetworkX
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- 🔍 A sophisticated simulation of Earth's ecosystems using cellular automata, modeling complex environmental interactions in a 2D grid.
- 🎓 Developed for the "Biological Computation" class at the Open University of Israel, earning a perfect score of
100 in 2024.
- 🌐 Demonstrates dynamic world modeling with real-time visualization and statistical analysis, offering insights into the interconnectedness of environmental factors.
- 🧬 Showcases advanced Python programming techniques, including encapsulation, context managers, and custom Enums for dynamic rule-based logic.
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🏷️ CellularAutomata , EnvironmentalSimulation , RealTimeVisualization
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🔍 Minimax Algorithm & Strategy Visualization: This project showcases a strategic stone-taking game against a computer, utilizing the minimax algorithm for decision making and NetworkX for visualizing the strategy tree.
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📈 Graph Theory Application: Through NetworkX, the project demonstrates an advanced application of graph theory in visualizing decision trees, aiding in understanding the algorithm's decision-making process.
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🤖 Interactive Gameplay with Tkinter: Incorporates Tkinter for the GUI, providing an interactive platform for gameplay and algorithm visualization.
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🎓 Educational Tool: Serves as an educational tool for understanding minimax algorithms and their applications in game theory and AI.
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📝 Comprehensive Documentation: Includes detailed explanations of the algorithm, code comments, and a guide on running the game and visualizing the strategy tree.
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🏷️ MinimaxAlgorithm , Trees , TkinterGUI , NetworkX , Backtracking , GameTheory
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🔍 Implements a neural network of two single-layer perceptrons for classifying 21-digit binary numbers based on the count of 'ones'.
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🧠 Showcases fundamental concepts of perceptron-based classification including weight initialization, prediction, training, and plotting decision boundaries.
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🖥️ Features an interactive GUI for easy interaction, prediction, and visualization of the perceptron classifier.
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📚 A demonstration of practical machine learning algorithms application, part of my software engineering portfolio.
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🏷️ NeuralNetworks , Perceptron , BinaryClassification , Numpy
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🔍 An advanced implementation of Restricted Boltzmann Machines (RBM) for the classification of the Iris dataset, demonstrating the potential of generative learning in machine learning and AI.
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🌐 Features a custom-built RBM model using Python and NumPy, without reliance on external libraries, emphasizing the project's focus on understanding and applying core machine learning principles.
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🎨 Includes a user-friendly GUI application for easy interaction with the model, facilitating dataset management, model training, and visualization of learning progress.
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📚 Part of a project for the Biological Computation course at the Open University of Israel, showcasing practical applications of RBMs in classifying complex datasets.
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🏷️ RBM , GenerativeLearning , MachineLearning , Python , NumPy
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🔍 A comprehensive Python implementation of decision trees, aimed at understanding and applying machine learning and AI principles.
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🌱 Converts Java-based algorithm to Python, maintaining the original structure and interface while achieving a perfect score in AI coursework.
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📊 Features practical examples, including tennis and loans datasets, to demonstrate the algorithm's versatility and accuracy.
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🛠️ Offers an in-depth look into algorithmic enhancements, data instance management, and dataset challenges, providing a rich learning resource.
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🏷️ DecisionTrees , DataScience , Information-gain , Entropy , NetworkX
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