Ahmed Gad received his B.Sc. degree with excellent with honors in information technology from the Faculty of Computers and Information (FCI), Menoufia University, Egypt, in July 2015. For being ranked first in his faculty, he was recommended to work as a teaching assistant in one of the Egyptian institutes in 2015 and then in 2016 to work as a teaching assistant and a researcher in his faculty. His current research interests include deep learning, machine learning, artificial intelligence, digital signal processing, and computer vision.
This tutorial discusses the confusion matrix, and how the precision, recall and accuracy are calculated, and how they relate to evaluating deep learning models.
For speeding up the calculation of the Levenshtein distance, this tutorial works on calculating using a vector rather than a matrix, which saves a lot of time. We’ll be coding in Java for this implementation.
This tutorial guides Android developers in preparing the popular library OpenCV for use. Using a step-by-step guide, the library will be imported into Android Studio and then can be used for performing any of the operations it supports, such as object detection, segmentation, tracking, and more.
Despite the benefits of federated learning, there are still ways of breaching a user’s privacy, even without sharing private data. In this article, we’ll review some research papers that discuss how federated learning includes this vulnerability.
This tutorial discusses how to use the genetic algorithm (GA) for reducing the feature vector extracted from the Fruits360 dataset in Python mainly using NumPy and Sklearn.
This tutorial builds artificial neural network in Python using NumPy from scratch in order to do an image classification application for the Fruits360 dataset
Evaluating Deep Learning Models: The Confusion Matrix, Accuracy, Precision, and Recall
Introduction to Federated Learning
Artificial Neural Networks Optimization using Genetic Algorithm with Python
Artificial Neural Network Implementation using NumPy and Image Classification