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Name: REDA RAFI
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Location: Maroc
Name: REDA RAFI
Type: User
Location: Maroc
Building Linux Python dependencies on a Mac to be included in your AWS Lambda function.
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.
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official code of F-COOPER
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.
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AWS AMPLIFY SERVELESS APP
Submanifold sparse convolutional networks
An Open Source Machine Learning Framework for Everyone
Null Repo for Test
Truth Table Generator
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)
Vitis AI is Xilinx’s development stack for AI inference on Xilinx hardware platforms, including both edge devices and Alveo cards.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.