2d-map-creation-of-environment-using-mobile-robot-

academicpages.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

aima-python

Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"

air-bird

This mobile application is useful to monitor the dynamic pollution sources integrated with google map API.

anomaly-detection-in-time-series-data-

Quarantine Project

apache-airflow-beam-tensorflow-examples

Various examples for TensorFlow Extended using Apache Beam and Airflow to create End2End Pipelines for Machine Learning.

arduinoble

ArduinoBLE library for Arduino

arduinocloudthing

arduinographics

arduinoiotcloud

arduinoiotcloudbearssl

arduinolowpower

Powersave features for SAMD boards

autoencoder_for_physical_layer

This is my attempt to reproduce and extend the results in the paper "An Introduction to Deep Learning for the Physical Layer" by Tim O'Shea and Jakob Hoydis

awesome-github-profile-readme-templates

This repository contains best profile readme's for your reference.

awesome-mlops

A curated list of references for MLOps

awesome-mlops-1

:sunglasses: A curated list of awesome MLOps tools

bhargav_web_portfolio

branch-test

testing the branch code

cart-pole-balancing-using-reinforcment-learning

Reinforcement Learning Example

chest-x-ray-classification-with-gradcam

Achieved 96.7% classification accuracy using transfer learning approach with VGG16 pre-trained model. We utilized Gradient based Class Activation Maps (GradCAM) to provide transparency for the decision taken by CNN classifier.

convolutional-neural-network

I have use Convolutional Neural Network to make a classification model for Fashion MNIST data set. After 200 epoch model has achieved 96.9% of validation Accuracy.

covid-19-assist-and-alert-system-caas-19-

covid-chestxray-dataset

We are building an open database of COVID-19 cases with chest X-ray or CT images.

covid-net

COVID-Net model for COVID-19 detection on COVIDx dataset

cs229-2018-autumn

All notes and materials for the CS229: Machine Learning course by Stanford University

csp520-computer-vision

deblurgan

Image Deblurring using Generative Adversarial Networks

deepdeblur_release

Deep Multi-scale CNN for Dynamic Scene Deblurring

deepdream

denoiser

Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.