ahhaque
I am a passionate ML applied scientist with a Ph.D. in computer science. I love to provide ML solutions at scale for solving real-world problems.
MicrosoftSeattle, WA
Pinned Repositories
djangoapp
The polls app from the official Django tutorial, that demonstrates how to build data-driven Python apps in Azure App Service.
ECHO
ECHO is a semi-supervised framework for classifying evolving data streams based on our previous approach SAND. The most expensive module of SAND is the change detection module, which has cubic time complexity. ECHO uses dynamic programming to reduce the time complexity. Moreover, ECHO has a maximum allowable sliding window size. If there is no concept drift detected within this limit, ECHO updates the classifiers and resets the sliding window. Experiment results show that ECHO achieves significant speed up over SAND while maintaining similar accuracy. Please refer to the paper (mentioned in the reference section) for further details.
FUSION
Efficient Multistream Classification using Direct DensIty Ratio Estimation
MachineLearningNotebooks
Python notebooks with ML and deep learning examples with Azure Machine Learning | Microsoft
MLOpsPython
msdocs-django-postgresql-sample-app
A sample Django app using PostgreSQL for the Azure App Service Web App + Database tutorial
MSR
MultiStream Regression
pyspark-cheatsheet
PySpark Cheat Sheet - example code to help you learn PySpark and develop apps faster
SAND
SAND: Semi-Supervised Adaptive Novel Class Detection and Classification over Data Stream
ahhaque's Repositories
ahhaque/SAND
SAND: Semi-Supervised Adaptive Novel Class Detection and Classification over Data Stream
ahhaque/ECHO
ECHO is a semi-supervised framework for classifying evolving data streams based on our previous approach SAND. The most expensive module of SAND is the change detection module, which has cubic time complexity. ECHO uses dynamic programming to reduce the time complexity. Moreover, ECHO has a maximum allowable sliding window size. If there is no concept drift detected within this limit, ECHO updates the classifiers and resets the sliding window. Experiment results show that ECHO achieves significant speed up over SAND while maintaining similar accuracy. Please refer to the paper (mentioned in the reference section) for further details.
ahhaque/FUSION
Efficient Multistream Classification using Direct DensIty Ratio Estimation
ahhaque/MSR
MultiStream Regression
ahhaque/djangoapp
The polls app from the official Django tutorial, that demonstrates how to build data-driven Python apps in Azure App Service.
ahhaque/MachineLearningNotebooks
Python notebooks with ML and deep learning examples with Azure Machine Learning | Microsoft
ahhaque/MLOpsPython
ahhaque/msdocs-django-postgresql-sample-app
A sample Django app using PostgreSQL for the Azure App Service Web App + Database tutorial
ahhaque/pyspark-cheatsheet
PySpark Cheat Sheet - example code to help you learn PySpark and develop apps faster