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Mikołaj Ryba's Projects

analiza-zasiegu-kampanii icon analiza-zasiegu-kampanii

Przesiębiorstwo rozważa podjęcie współpracy z 10 gumisiami-blogerami (inluencerami) w celu promocji swojego nowego produktu P2.

anomalydetection-implementation icon anomalydetection-implementation

This repository contains an implementation of an anomaly detection algorithm using Gaussian distribution. The algorithm can be used to identify and remove anomalies from data sets.

antcolonyoptimization-hyperparametersearch icon antcolonyoptimization-hyperparametersearch

In this project, I use optuna library to perform a hyperparameter search for Professor Wojciech Broniowski's implementation of Ant Colony Optimization (ACO) algorithm. I use the optuna library to optimize the hyperparameters and improve the performance of the algorithm.

bipedalwalker-reinforcementlearning icon bipedalwalker-reinforcementlearning

In this project I create agent for the BipedalWalker environment using the Proximal Policy Optimization (PPO) algorithm from the stablebaselines3 library. The agent is trained to navigate the BipedalWalker environment, which is a simulated robot with two legs.

cartpole-reinforcementlearning icon cartpole-reinforcementlearning

In this project, I created an agent to solve the CartPole task using the stablebaselines3 library. CartPole is a problem from the OpenAI Gym catalog, in which the goal is to maintain balance of a wooden pole using motors attached to its ends. The agent must decide whether to move the pole left or right to maintain balance.

dimonds-knapsack-problem icon dimonds-knapsack-problem

The knapsack problem is a classic optimization problem in computer science and operations research. It involves selecting a subset of items to pack into a knapsack with a limited weight capacity, while maximizing the total value of the selected items.

flappybird-reinforcmentlearning icon flappybird-reinforcmentlearning

This project implements an agent for playing the FlappyBird game in a browser using the Deep Q-Network (DQN) algorithm from the stablebaselines3 library. The agent is trained to learn the optimal actions to take at each step in the game in order to maximize the score.

gamefrozenlake-in-csharp-with-qlearningagent icon gamefrozenlake-in-csharp-with-qlearningagent

This project is a C# implementation of the popular game "Frozen Lake" and an AI agent that can play the game using the Q-learning algorithm. The game consists of a grid of tiles, some of which are safe to walk on, while others will cause the player to receive damage.

gausianeliminationmethod-implementation icon gausianeliminationmethod-implementation

GausianEliminationMethod-Implementation is a project that demonstrates the implementation of the Gaussian elimination method in Python. This method is used to solve systems of linear equations and involves manipulating the equations in a specific way to eliminate variables and obtain a unique solution.

gaussseideliteration-implementation icon gaussseideliteration-implementation

GaussSeidelIteration-Implementation is a project that demonstrates the implementation of the Gauss-Seidel iterative numerical method in the C programming language. This method is used to solve systems of linear equations and is known for its convergence properties and efficiency.

gender-classification icon gender-classification

In this project, I created a classifier using the sklearn library to predict the gender of an individual based on tabular data. The classifier was trained using supervised learning techniques.

geneticalgorithm-hyperparametersearch icon geneticalgorithm-hyperparametersearch

In this project, I use optuna library to perform a hyperparameter search for Professor Wojciech Broniowski's implementation of Genetic Algorythm algorithm, as well as comparing this algorythm to AntColonyOptimization. I use the optuna library to optimize the hyperparameters and improve the performance of the algorithm.

irisdataset-classification icon irisdataset-classification

This repository contains experiments on the Iris dataset using various scikit-learn classifiers. The goal of these experiments is to evaluate the performance of different classification algorithms on this widely-used dataset in order to compare their accuracy and identify the best approach for the given task.

k-means-clustering-implementation icon k-means-clustering-implementation

This repository contains an implementation of the K-Means clustering algorithm in Python. K-Means is an unsupervised machine learning algorithm that finds clusters in an N-dimensional space. The implementation provided in this repository allows users to apply K-Means to their own data sets and visualize the resulting clusters.

k-nearestneighbors-implementation icon k-nearestneighbors-implementation

This is a project that implements the K-Nearest Neighbors (KNN) algorithm in Python. KNN is a machine learning algorithm used for classification or regression based on training data, and is an unsupervised learning model. This implementation allows you to train a KNN model on training data and classify new data.

linearregression-implementation icon linearregression-implementation

This repository contains implementations of linear regression using both gradient descent and linear algebra techniques. The goal of these implementations is to provide a thorough understanding of the linear regression algorithm and its various approaches to solving for the optimal model parameters.

lunarlander-reinforcementlearning icon lunarlander-reinforcementlearning

In this project, I created an agent using the PPO algorithm from stable baselines3 to complete a task in the LunarLander environment. The agent was trained using reinforcement learning techniques to maximize its performance in the task. The resulting model was able to achieve a high level of success in the LunarLander environment.

nn_polish icon nn_polish

Sieci neuronowe dla początkujacych w Pythonie

principalcomponentanalysis-implementation icon principalcomponentanalysis-implementation

This project is an implementation of Principal Component Analysis (PCA) in Python. PCA is a technique for dimensionality reduction and data visualization that aims to find the most important underlying patterns in a dataset.

programmingwithllms icon programmingwithllms

This repository contains an engineering thesis that tests the ChatGPT-4 language model in machine learning tasks (document in Polish).

python-exercises icon python-exercises

This repository contains a collection of Python exercises that I have accumulated over the months of studying this leanguage. These exercises cover various topics in the Python programming language and range in difficulty from beginner to intermediate level.

samplepythonclasses-objectorientedprogrammingproject icon samplepythonclasses-objectorientedprogrammingproject

SamplePythonClasses-ObjectOrientedProgrammingProject is a project that demonstrates the concepts of object-oriented programming using the Python programming language. The project covers topics such as class inheritance, polymorphism, and encapsulation, and includes examples of real-world applications of these concepts.

simplified-data-encryption-standard icon simplified-data-encryption-standard

This repository contains a Jupyter Notebook that provides a straightforward, step-by-step implementation of the Simplified Data Encryption Standard (S-DES).

sonicthehedgehog2-reinforcmentlearning icon sonicthehedgehog2-reinforcmentlearning

This project implements an agent for playing the SonicTheHedgehog2 game from a ROM file using the Proximal Policy Optimization (PPO) algorithm from the stablebaselines3 library. The agent is trained to learn the optimal actions to take at each step in the game in order to complete the level and maximize the score.

supermariobros-reinforcementlearning icon supermariobros-reinforcementlearning

This project implements an agent for playing the SuperMarioBros game using the Proximal Policy Optimization (PPO) algorithm from the stablebaselines3 library. The agent is trained to learn the optimal actions to take at each step in the game in order to complete the level and maximize the score.

travellingsalesmanproblem-in-polishvoivodeships icon travellingsalesmanproblem-in-polishvoivodeships

This project uses python-tsp and geopandas library to find the shortest path between district cities within chosen voivodeships in Poland, it also provides a map visualization of the solution. Script can be adapted to solve TSP problem in any region by replacing shapefiles.

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