abdelrahman-amen

applying-different-unsupervised-learning-techniques

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.

applying-kfold-from-scratch

building-neural_network

car_plate_detection_using_yolov8

centroid-in-pattern_recognition

This repository implements centroid-based pattern recognition, extracting features from images using grid cell centroids for classification in computer vision and image processing.

chain-code-in-patter-recognition

cifar10_using_convolutional_neural_network

classification

dynamic_neural_network

fuzzy-c-means-clustering-from-scratch

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

Hexapod Robot

housing-price

Predicting housing prices with machine learning regression models. This project implements Linear Regression, Random Forest, and Decision Tree models for accurate predictions.

inception-network-from-scratch-and-built_in

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.

iris_flower-data-set

k-nearest-neighbors-knn-from-scratch-and-built_in

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.

lennet-5

LeNet-5 Image Classification project demonstrates the power of the LeNet convolutional neural network for character and digit recognition in grayscale images.

logistic_regression_from_scratch

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.

medical-cost-personal-datasets

mushroom-classification

optimizers

random-data_set

regression

resnet50-from_scratch_and_built_in

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.

svm-linearly_separable-

svm-non-linearly_-separable-

td_idf-from-scratch-and-built_in

titanic-survival-prediction

The Titanic dataset includes passenger information such as survival status, ticket class, gender, age, family relations aboard, fare, cabin, and port of embarkation. It's widely used for predictive modeling to understand survival patterns based on passenger attributes.

vgg16-from-scratch-and-built_in

This project implements the powerful VGG-16 convolutional neural network for image classification, showcasing its efficiency with 3x3 filters, same padding, stride of 1, and 2x2 max-pooling for superior pattern recognition in diverse images.

web_scraping-and-text_processing-nlp-

Web scraping involves extracting data from websites. Text processing techniques like tokenization, stemming, lemmatization, and removing stopwords refine raw text for analysis.