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IamHussain's Projects

alexapi icon alexapi

Alexa client for all your devices! # No active development. PRs welcome # consider https://github.com/respeaker/avs instead

alpine-node icon alpine-node

Minimal Node.js Docker Images built on Alpine Linux

autorccar icon autorccar

OpenCV Python Neural Network Autonomous RC Car

avs icon avs

python implementation of alexa voice service app, 支持DuerOS

bootcamp-2021 icon bootcamp-2021

Fusing Serverless Cloud Computing, Infrastructure as Code, Graph Databases, AI, and IoT Technologies and preparing for Operation Unicorn Startups

brain-tumor-detection icon brain-tumor-detection

In this repository, I have explained the process of brain tumor detection using python and sklearn

custom-svm icon custom-svm

Custom implementation of Support Vector Machines using Python and NumPy

datasets icon datasets

NCBI Datasets is an experimental resource for finding and building datasets

discovery icon discovery

Discover the world of microcontrollers through Rust!

end-to-end-time-series icon end-to-end-time-series

This repository hosts code for my Time Series videos part of playlist here - https://www.youtube.com/playlist?list=PL3N9eeOlCrP5cK0QRQxeJd6GrQvhAtpBK

face-recognition-and-face-verification icon face-recognition-and-face-verification

Here you will build a face recognition system. Many of the ideas presented here are from FaceNet. In lecture, we also talked about DeepFace. Face recognition problems commonly fall into two categories: Face Verification - "is this the claimed person?". For example, at some airports, you can pass through customs by letting a system scan your passport and then verifying that you (the person carrying the passport) are the correct person. A mobile phone that unlocks using your face is also using face verification. This is a 1:1 matching problem. Face Recognition - "who is this person?". For example, the video lecture showed a face recognition video (https://www.youtube.com/watch?v=wr4rx0Spihs) of Baidu employees entering the office without needing to otherwise identify themselves. This is a 1:K matching problem. FaceNet learns a neural network that encodes a face image into a vector of 128 numbers. By comparing two such vectors, you can then determine if two pictures are of the same person.

facenet-face-recognition icon facenet-face-recognition

A face recognition demo performed by feeding images of faces recorded by a webcam into a trained FaceNet network to determine the identity of the face

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