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asumobicarg50

Remote controlled car project with a set of other functions using Arduino and some sensors

attendanceapp

a solution that would allow instructors to take the attendance of the session by only taking some pictures of the attending students.

avr-digitalclock

covergenerator

cse-microcontrollers-counter

cse323-sna

deepface

Keras implementation of the renowned publication "DeepFace: Closing the Gap to Human-Level Performance in Face Verification" by Taigman et al. Pre-trained weights on VGGFace2 dataset.

deepfake-keras-implementation

implementing DCGAN or Deep Convolutional Generative Adversarial Network, and training the network to generate realistic looking synthesized images

enhancing-image-resolution-with-autoencoders

use Keras with Tensorflow as its backend to train an autoencoder, and use this deep learning powered autoencoder to significantly enhance the quality of images. That is, this neural network will create high-resolution images from low-res source images.

facial_keypoints_detection

First project for CVND: facial keypoint detection.

hil-facial-emotion-recognition-system

image-captioning-project

implement_slam

Landmark Detection and Tracking (SLAM) project for CVND

mmf

A modular framework for vision & language multimodal research from Facebook AI Research (FAIR)

overlappingrectangles

C++ program that takes some rectangles and group them into the minimum number of groups where each group has non overlapping rectangles

pci

Verilog behavioural code for PCI bus Arbiter and Devices communication.

polygonsproject

ppi855

predicting-house-prices-with-regression

Solution for the House prices problem on kaggle

real-time-face-expression-recognition

build and train a convolutional neural network (CNN) in Keras from scratch to recognize facial expressions

sagemaker-deployment

Code and associated files for the deploying ML models within AWS SageMaker

se2018g10

siamese-network-keras-implementation

implement a Triplet Loss function, create a Siamese Network, and train the network with the Triplet Loss function. With this training process, the network will learn to produce Embedding of different classes from a given dataset in a way that Embedding of examples from different classes will start to move away from each other in the vector space.

smart-home

we'll be using 3 layered Architecture: Layer for the Application, Layer for the Drivers, and a MCAL Layer

tinylanguageparser

tinylanguagescanner

twits-identifier

Classification of tweets from two twitter users to determine the probability that a particular tweet came from a particular user.