Abdelrahman Amin's Projects
Applying various clustering techniques to the dataset, and my primary goal is to identify and choose the most effective method that best captures the underlying patterns in the data.
This repository implements centroid-based pattern recognition, extracting features from images using grid cell centroids for classification in computer vision and image processing.
Fuzzy C-Means (FCM) is a clustering algorithm that assigns membership degrees to data points, allowing for soft assignment to clusters. It offers flexibility, robustness to noise, interpretability, scalability, and versatility in various domains such as pattern recognition and data mining.
Hexapod Robot
Predicting housing prices with machine learning regression models. This project implements Linear Regression, Random Forest, and Decision Tree models for accurate predictions.
Explore the Inception Network, a powerful deep learning architecture designed for image classification. Uncover the efficiency of 1x1 convolutions, strategically used to reduce computational costs and capture intricate features at different scales, revolutionizing the way neural networks process information.
KNN is a basic machine learning algorithm used for classification and regression tasks. It predicts the class of a new data point based on the majority class of its nearest neighbors. KNN is simple, non-parametric, and learns directly from the training data without explicit training.
LeNet-5 Image Classification project demonstrates the power of the LeNet convolutional neural network for character and digit recognition in grayscale images.
Logistic regression is a statistical technique primarily used for binary classification tasks. It predicts the probability of a binary outcome based on one or more predictor variables. Unlike linear regression, which predicts continuous outcomes, logistic regression deals with categorical outcomes.
ResNet-50, with 50 layers, excels in image classification by addressing the vanishing gradient problem. Skip connections facilitate seamless information flow, empowering the model for intricate feature learning. Its unique architecture makes ResNet-50 a robust choice for complex pattern recognition.