Sahar Hamdi (sahar-hamdi)

sahar-hamdi

Geek Repo

Company:Student at Faculty of Computers Science and Artificial Intelligence

Location:Giza-Egypt

Home Page:https://codeforces.com/profile/sahar112- https://www.linkedin.com/in/sahar-hamdi-9467b8224

Github PK Tool:Github PK Tool

Sahar Hamdi's repositories

Language:Jupyter NotebookStargazers:2Issues:0Issues:0
Stargazers:1Issues:0Issues:0

Feature-Extraction-using-Chain-Code-with-MNIST

This repository contains code for performing image classification on the MNIST dataset using Chain Code feature extraction technique and machine learning classifiers.

Language:Jupyter NotebookStargazers:1Issues:0Issues:0
Language:Jupyter NotebookStargazers:1Issues:0Issues:0

PCA-Implementation-from-Scratch-using-Coloredimage

- Compress a colored image using PCA, then visualize the compresses image - Decompress the compressed image and visualize it

Language:Jupyter NotebookStargazers:1Issues:0Issues:0

Quadruped-Robot

The Q1 Mini Quadruped Robot 2.0, developed by Jason Workshop, represents a significant advancement in the field of versatile and agile robotics.

Stargazers:1Issues:0Issues:0
Stargazers:1Issues:0Issues:0

Basic-Virtual-Library-System-OOP

Design and Implementation of a virtual library system using the principles of Object-Oriented Programming (OOP). The system will allow users to manage a collection of books and perform operations like adding books, removing books, and searching for books by various criteria. I used C++ for System Implementation in this Assignment.

Language:C++Stargazers:0Issues:0Issues:0
Stargazers:0Issues:0Issues:0
Language:C++Stargazers:0Issues:0Issues:0

Disney-Characters-Classification

Classification of Disney Characters using CNN

Language:Jupyter NotebookStargazers:0Issues:0Issues:0
Language:Jupyter NotebookStargazers:0Issues:0Issues:0

github-profile-readme-generator

🚀 Generate GitHub profile README easily with the latest add-ons like visitors count, GitHub stats, etc using minimal UI.

License:Apache-2.0Stargazers:0Issues:0Issues:0
Language:Jupyter NotebookStargazers:0Issues:0Issues:0
Language:Jupyter NotebookStargazers:0Issues:0Issues:0
Language:Jupyter NotebookStargazers:0Issues:0Issues:0
Language:Jupyter NotebookStargazers:0Issues:0Issues:0

LeNet-Architecture-using-gray-scaled-images-vs-LeNet-Architecture-using-Colored-Images

-Load two common datasets -Use any needed pre-processing function to analyze dataset -Use LeNet-5 to complete the process of classification -Print LeNet-5 architecture -Print number of Trainable parameters in each layer -Print confusion matrix relative to testing samples -Print precision, recall, f1_score -Comment on your results

Language:Jupyter NotebookStargazers:0Issues:0Issues:0
Language:C++Stargazers:0Issues:0Issues:0

Logistic-Regression-from-Scratch-and-Cross-Validation-From-Scratch-

1. Load MNIST dataset. 2. Subset your data to use only class 0 and class1 for the next steps. 3. Standardize your dataset 4. Divide data into training and validation set using 10-fold cross validation method 5. Implement Logistic Regression with different values for learning rate 6. Report difference accuracy for the different learning rate.

Language:Jupyter NotebookStargazers:0Issues:0Issues:0

Naive-Bayes-Classifier-from-Scratch-on-IRIS

Implementation of Naive Bayes on Normal Distribution from Scratch using Iris Dataset.

Language:Jupyter NotebookStargazers:0Issues:0Issues:0
Language:Jupyter NotebookStargazers:0Issues:0Issues:0
Stargazers:0Issues:0Issues:0

PCA-Implementation-from-Scratch-with-gray-scaled-image

Compress a gray scale image using PCA, then visualize the compresses image - Decompress the compressed image and visualize it using PCA From Scratch

Language:Jupyter NotebookStargazers:0Issues:0Issues:0

Perceptron-Classifier-Implementation

1- Load the data Banknote_authentication.csv and shuffle it

Language:Jupyter NotebookStargazers:0Issues:0Issues:0

Quadropod

A robot with 4 legs controlled via mobile application

Stargazers:0Issues:0Issues:0
Stargazers:0Issues:0Issues:0

Spotify-Tracks-Analytics-and-Prediction

In this project, we aim to perform exploratory data analysis (EDA) on the Spotify dataset to gain insights into various aspects of the tracks. We will visualize distributions, explore relationships between features, investigate correlations between numerical variables, and Build a Prediction Models.

Language:Jupyter NotebookStargazers:0Issues:0Issues:0

Web-Crawler-Information-Retrieval-

1- A web crawler that crawl wikipedia starting from the following 2 seeded https://en.wikipedia.org/wiki/List_of_pharaohs 2- Build the inverted index for visited pages 3- get a query ( set of a number of words) 4- compute the cosine similarity between each file and the query 5- rank the top k=10 files according to the value of the cosin similarity

Language:JavaStargazers:0Issues:0Issues:0