RedaRafi

amplify-lambda-py-build

Building Linux Python dependencies on a Mac to be included in your AWS Lambda function.

Breast-Cancer-Detection-

Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall. There were over 2 million new cases in 2018, making it a significant health problem in present days. The key challenge in breast cancer detection is to classify tumors as malignant or benign. Malignant refers to cancer cells that can invade and kill nearby tissue and spread to other parts of your body. Unlike cancerous tumor(malignant), Benign does not spread to other parts of the body and is safe somehow. Deep neural network techniques can be used to improve the accuracy of early diagnosis significantly. Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called an artificial neural network. A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other. The pre-processing required in a ConvNet is much lower as compared to other classification algorithms.

Compte_Operation_J2EE_Spring

Spring APP gestion des compte bancaire Junior

Deep-Learning

F-COOPER

official code of F-COOPER

Face-Recognition-using-MTCNN

Deep learning advancements in recent years have enabled widespread use of face recognition technology. This article tries to explain deep learning models used for face recognition and introduces a simple framework for creating and using a custom face recognition system. Formally, Face Recognition is defined as the problem of identifying or verifying faces in an image. How exactly do we recognise a face in an image? Face recognition can be divided into multiple steps. The image below shows an example of a face recognition pipeline. Face recognition pipeline. Face detection — Detecting one or more faces in an image. Feature extraction — Extracting the most important features from an image of the face. Face classification — Classifying the face based on extracted features.MTCNN MTCNN or Multi-Task Cascaded Convolutional Neural Networks is a neural network which detects faces and facial landmarks on images. It was published in 2016 by Zhang et al.

FaculTy

MNIST_APP

PageRank

Segmentation_Using_CNN-

sign translator

RedaRafi/amplify-lambda-py-build

Building Linux Python dependencies on a Mac to be included in your AWS Lambda function.

RedaRafi/Breast-Cancer-Detection-

Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall. There were over 2 million new cases in 2018, making it a significant health problem in present days. The key challenge in breast cancer detection is to classify tumors as malignant or benign. Malignant refers to cancer cells that can invade and kill nearby tissue and spread to other parts of your body. Unlike cancerous tumor(malignant), Benign does not spread to other parts of the body and is safe somehow. Deep neural network techniques can be used to improve the accuracy of early diagnosis significantly. Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called an artificial neural network. A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other. The pre-processing required in a ConvNet is much lower as compared to other classification algorithms.

RedaRafi/Compte_Operation_J2EE_Spring

Spring APP gestion des compte bancaire Junior

RedaRafi/Deep-Learning

RedaRafi/F-COOPER

official code of F-COOPER

RedaRafi/Face-Recognition-using-MTCNN

Deep learning advancements in recent years have enabled widespread use of face recognition technology. This article tries to explain deep learning models used for face recognition and introduces a simple framework for creating and using a custom face recognition system. Formally, Face Recognition is defined as the problem of identifying or verifying faces in an image. How exactly do we recognise a face in an image? Face recognition can be divided into multiple steps. The image below shows an example of a face recognition pipeline. Face recognition pipeline. Face detection — Detecting one or more faces in an image. Feature extraction — Extracting the most important features from an image of the face. Face classification — Classifying the face based on extracted features.MTCNN MTCNN or Multi-Task Cascaded Convolutional Neural Networks is a neural network which detects faces and facial landmarks on images. It was published in 2016 by Zhang et al.

RedaRafi/FaculTy

RedaRafi/MNIST_APP

RedaRafi/PageRank

RedaRafi/Segmentation_Using_CNN-

sign translator

RedaRafi/Segmentation_using_Tensor

RedaRafi/Sentiment_Analysis_and_Machine_Learning

RedaRafi/serverless-airline-booking-system

AWS AMPLIFY SERVELESS APP

RedaRafi/SparseConvNet

Submanifold sparse convolutional networks

RedaRafi/tensorflow

An Open Source Machine Learning Framework for Everyone

RedaRafi/test-Null-

Null Repo for Test

RedaRafi/TruthTableGenerator

Truth Table Generator

RedaRafi/Tweet-Hespresse-Preprocessing-NLP-

RedaRafi/unsupervised-Classification-NLP-using-3grams

Deux méthodes de classification exploitant des approches non-supervisées Implémentation de la classification tf-idf en se basant sur des tokens de caractères (3-grams)

RedaRafi/Velib-Predict

RedaRafi/Vitis-AI

Vitis AI is Xilinx’s development stack for AI inference on Xilinx hardware platforms, including both edge devices and Alveo cards.