Pinned Repositories
gradio
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
awesome-virtual-try-on
A curated list of awesome research papers, projects, code, dataset, workshops etc. related to virtual try-on.
BERT-NER-TF
Named Entity Recognition with BERT using TensorFlow 2.0
EditAnything
Edit anything in images powered by segment-anything, ControlNet, StableDiffusion, etc.
pyspark-model-plus
Enhancements to commonly used pyspark functions for modelling
RajarshiBhadra
Config files for my GitHub profile.
Robyn
Robyn is an experimental, automated and open-sourced Marketing Mix Modeling (MMM) package from Facebook Marketing Science. It uses various machine learning techniques (Ridge regression with cross validation, multi-objective evolutionary algorithm for hyperparameter optimisation, gradient-based optimisation for budget allocation etc.) to define media channel efficiency and effectivity, explore adstock rates and saturation curves. It's built for granular datasets with many independent variables and therefore especially suitable for digital and direct response advertisers with rich dataset.
sagemaker-custom-model
Customized Modelling containers for SageMaker
sagemaker-spark
Sagemaker Spark Customized repo for spark-nlp
RajarshiBhadra's Repositories
RajarshiBhadra/awesome-virtual-try-on
A curated list of awesome research papers, projects, code, dataset, workshops etc. related to virtual try-on.
RajarshiBhadra/BERT-NER-TF
Named Entity Recognition with BERT using TensorFlow 2.0
RajarshiBhadra/EditAnything
Edit anything in images powered by segment-anything, ControlNet, StableDiffusion, etc.
RajarshiBhadra/pyspark-model-plus
Enhancements to commonly used pyspark functions for modelling
RajarshiBhadra/RajarshiBhadra
Config files for my GitHub profile.
RajarshiBhadra/Robyn
Robyn is an experimental, automated and open-sourced Marketing Mix Modeling (MMM) package from Facebook Marketing Science. It uses various machine learning techniques (Ridge regression with cross validation, multi-objective evolutionary algorithm for hyperparameter optimisation, gradient-based optimisation for budget allocation etc.) to define media channel efficiency and effectivity, explore adstock rates and saturation curves. It's built for granular datasets with many independent variables and therefore especially suitable for digital and direct response advertisers with rich dataset.
RajarshiBhadra/sagemaker-custom-model
Customized Modelling containers for SageMaker
RajarshiBhadra/sagemaker-spark
Sagemaker Spark Customized repo for spark-nlp