Italian COVID-19 Statistics Visualisation
URL: https://github.com/vincenzosantopietro/Covid-19-Statistics
Description:
This project is just a way to show COVID-19 statistics for italy. Tools used: Scala, Spark, Grafana, InfluxDB
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Artificial Intelligence
Get the most out of your training, scoring, algorithms and frameworks on Intel® architecture for Deep Learning and Artificial Intelligence.
Evo Norm layers in TensorFlow 2
URL: https://github.com/sayakpaul/EvoNorms-in-TensorFlow-2
Description:
Presents implementations of EvoNormB0 and EvoNormS0 layers as proposed in Evolving Normalization-Activation Layers by Liu et al. The authors showed the results with these layers tested on MobileNetV2, ResNets, MnasNet, and EfficientNets. However, I tried a Mini Inception architecture.
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V Change - Care for Homeless
URL: https://github.com/RaguRamanTB/Homeless-Eradicator
Description:
which will let anyone to spot the "Homeless" people and to connect them with appropriate caretakers. Also, the application lets the users to register as a volunteer and to contribute to society. The app also enables anyone to donate funding as a registered user or anonymously.
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isolalert
URL: https://github.com/cabelo/isolalert
Description:
Project created to analyze the behavior of the population, calculate the necessary distance between people to avoid contagion (COVID-19) and statistical processing of the number of people and vehicles in a given region.
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Image Adversaries 101
URL: https://github.com/sayakpaul/Image-Adversaries-101
Description:
Shows how to create basic image adversaries, and train adversarially robust image classifiers (to some extent).
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Telecommunication Churn Customer Prediction
URL: https://github.com/Awwal01/Churn-Customer-Prediction.git
Description:
understanding telecommunication customer behaviour to retain customers and prevent customers from churning
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Vyaadhi
URL: https://github.com/HarshCasper/Vyaadhi
Description:
This repository consists of the various Jupyter Notebooks that were written to perform analysis on the different Open-Source Datasets available on Health Parameters and different disease, namely: Breast Cancer, Diabetes Analysis, Heart Disease, Kidney Disease and Liver Disease.
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Ling API
URL: https://github.com/HarshCasper/Ling-API
Description:
An Application Programming Interface (API) using Flask Backend Micro Web Framework and Swagger UI to predict the gender of a person given their name using a Long Short Term Memory (LSTM) Neural Network developed using Keras and Tensorflow.
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Malaria Detection
URL: https://github.com/HarshCasper/Malaria-Detection
Description:
🦟 😷Classifying Malarial Cell Images using Tensorflow and deployed on a Flask Web Server to perform Real-Time Classification 🦠🔥
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AirPol
URL: https://github.com/Atharva-Peshkar/AirPol-Air-pollutant-concentration-prediction-
Description:
AirPol is a Machine Learning based project that aims to use various meteorological, geographical and temporal factors along with the current air pollutant concentration to accurately predict concentration of air pollutants in advance.
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TexGen
URL: https://github.com/Atharva-Peshkar/gpt-2
Description:
TexGen is a transformer based model that used the OpenAI GPT-2 under the hood to predict the next word in a sentence based in the previous word. This can be used to generate sentences or even articles, almost indistinguishable from those written by humans.
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Fighting Novel Coronavirus COVID-19 with Data Science & Machine Learning.
URL: https://github.com/azeemx/fighting_covid19_with_ds_ml.git
Description:
In December 2019, A novel Coronavirus was found in a seafood wholesale market located in Wuhan, China., On 11 February 2020, WHO announced a name for the new coronavirus disease: COVID-19., And was recognised as a pandemic on 11 March 2020.
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Brain Hemorrhage Detection Using Machine Learning
URL: https://github.com/DEBANJANAB/Brain-Hemorrhage-detection-in-CT-imaging
Description:
Intracranial Hemorrhage is referred to the type of bleeding inside the cranium of the skull, resulting from accidents or stroke mainly. According to survey, about 2.5 per 10,000 people are affected by ICH every year. About 44% of those affected die every year.
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ORM One Remote Monitor
URL: https://github.com/cabelo/orm
Description:
COVID-19 will change people's habits. We will use teaching and remote work (home office) more frequently, schools and companies will need to measure student performance and employee commitment.
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Image generation with GANs
URL: https://github.com/Vectoryzed/
Description:
This projects aimed to increase my skill in generating images using Generative Adversarial Networks. It reiles on Google TensorFlow 2.0.
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Intel Movidius Neural Compute Stick(NCS) with Raspberry Pi-3 for Image Classification
URL: https://github.com/akhilaku/Image-Classifier-using-Intel-Movidius-NCS-
Description:
For developing Deep Learning(DL) interference applications at the edge, we can use Intel's energy-efficient and low-cost Movidius USB stick. In this project we are going to use Intel Movidius Neural Compute USB stick with Raspberry Pi-3 for image classification application.
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Automatic Detection of Liver Cancer in WSI
URL: https://github.com/sushantag9/Automated-Detction-of-Liver-cancer-in-WSI
Description:
Detection and Segmentation of liver tumor in the given Whole Slide Image(WSI). Speeding up the process by using Intel® Distribution for Python* and Intel® Performance Libraries on the GPU with parallel processing
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Long Term Water Capacity Planning of Mumbai
URL: https://github.com/naidukarthi2193/deep_blue/tree/master/NEW
Description:
Even after having abundant rainfall Mumbai faces acute water shortages in the remaining months due to ever-increasing demand and improper management of water sources. We aim to improve this situation by Data Analytics and ML Models
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Treatise of Medical Image Processing (TMIP) v0.2.0
URL: https://github.com/TebogoNakampe/TMIP-2019-nCoV-Recognition.git
Description:
Experiment 2: Coronavirus (2019-nCoV infection) Recognition using Deep Neural Networks for Computer Tomography (CT) image analysis.
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Image recognition using Deep learning with Intel
URL: https://github.com/akhilaku/Image-recognition-by-Deep-Learning-using-MATLAB-softwares
Description:
We know that an effective accurate Deep Learning framework can be only done with advanced processors. When a deep learning framework is done with a very high-level programming language like MATLAB it becomes more complicated, still it can be done efficiently by any type of processors.
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