Author Archives: Nikita Shiledarbaxi - Page 6

25 Feb

Guide To Pastas: A Python Framework For Hydrogeological Time Series Analysis

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image-20312

Introduction to Pastas Pastas is an open-source Python framework designed for processing, simulation and analysis of hydrogeological time series models. It has built-in tools for statistically analyzing, visualizing and optimizing such models. It was introduced by Raoul A. Collenteur, Mark Bakker, Ruben Calje, Stijn A. Klop and Frans Schaars in an article published by the…

The post Guide To Pastas: A Python Framework For Hydrogeological Time Series Analysis appeared first on Analytics India Magazine.

25 Feb

Guide To Pastas: A Python Framework For Hydrogeological Time Series Analysis

image-20313
image-20313

Introduction to Pastas Pastas is an open-source Python framework designed for processing, simulation and analysis of hydrogeological time series models. It has built-in tools for statistically analyzing, visualizing and optimizing such models. It was introduced by Raoul A. Collenteur, Mark Bakker, Ruben Calje, Stijn A. Klop and Frans Schaars in an article published by the…

The post Guide To Pastas: A Python Framework For Hydrogeological Time Series Analysis appeared first on Analytics India Magazine.

25 Feb

Guide To Pastas: A Python Framework For Hydrogeological Time Series Analysis

image-20314
image-20314

Introduction to Pastas Pastas is an open-source Python framework designed for processing, simulation and analysis of hydrogeological time series models. It has built-in tools for statistically analyzing, visualizing and optimizing such models. It was introduced by Raoul A. Collenteur, Mark Bakker, Ruben Calje, Stijn A. Klop and Frans Schaars in an article published by the…

The post Guide To Pastas: A Python Framework For Hydrogeological Time Series Analysis appeared first on Analytics India Magazine.

25 Feb

Guide To Pastas: A Python Framework For Hydrogeological Time Series Analysis

image-20315
image-20315

Introduction to Pastas Pastas is an open-source Python framework designed for processing, simulation and analysis of hydrogeological time series models. It has built-in tools for statistically analyzing, visualizing and optimizing such models. It was introduced by Raoul A. Collenteur, Mark Bakker, Ruben Calje, Stijn A. Klop and Frans Schaars in an article published by the…

The post Guide To Pastas: A Python Framework For Hydrogeological Time Series Analysis appeared first on Analytics India Magazine.

25 Feb

Guide To Pastas: A Python Framework For Hydrogeological Time Series Analysis

image-20316
image-20316

Introduction to Pastas Pastas is an open-source Python framework designed for processing, simulation and analysis of hydrogeological time series models. It has built-in tools for statistically analyzing, visualizing and optimizing such models. It was introduced by Raoul A. Collenteur, Mark Bakker, Ruben Calje, Stijn A. Klop and Frans Schaars in an article published by the…

The post Guide To Pastas: A Python Framework For Hydrogeological Time Series Analysis appeared first on Analytics India Magazine.

23 Feb

Guide To Pyro – A Deep Probabilistic Programming Language

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image-20214

Pyro is a state-of-the-art programming language for deep probabilistic modelling. It is a flexible and scalable probabilistic programming language (PPL). It unifies the modern concepts of deep learning and Bayesian modelling. It has been written in Python and built on top of Pytorch. The Uber AI Labs introduced it in 2017. A team now maintains…

The post Guide To Pyro – A Deep Probabilistic Programming Language appeared first on Analytics India Magazine.

23 Feb

Guide To Pyro – A Deep Probabilistic Programming Language

image-20212
image-20212

Pyro is a state-of-the-art programming language for deep probabilistic modelling. It is a flexible and scalable probabilistic programming language (PPL). It unifies the modern concepts of deep learning and Bayesian modelling. It has been written in Python and built on top of Pytorch. The Uber AI Labs introduced it in 2017. A team now maintains…

The post Guide To Pyro – A Deep Probabilistic Programming Language appeared first on Analytics India Magazine.

23 Feb

Guide To Pyro – A Deep Probabilistic Programming Language

image-20213
image-20213

Pyro is a state-of-the-art programming language for deep probabilistic modelling. It is a flexible and scalable probabilistic programming language (PPL). It unifies the modern concepts of deep learning and Bayesian modelling. It has been written in Python and built on top of Pytorch. The Uber AI Labs introduced it in 2017. A team now maintains…

The post Guide To Pyro – A Deep Probabilistic Programming Language appeared first on Analytics India Magazine.

22 Feb

Guide To Real-time Object Detection Model Deployment Using Streamlit

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image-20183

Streamlit is an open-source Python library using which data scientists can build Machine learning from scratch. It also enables building various ML tools for visualization and analysis of the experiments’ data and output in an interactive app framework. It is used in the ML domain for several projects ranging from simple to complex ones. This…

The post Guide To Real-time Object Detection Model Deployment Using Streamlit appeared first on Analytics India Magazine.

18 Feb

Guide to TimeSynth – A Python Library For Synthetic Time Series Generation

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image-20098

Introduction TimeSynth is a powerful open-source Python library for synthetic time series generation, so is its name (Time series Synthesis). It was introduced by J. R. Maat, A. Malali and P. Protopapas as “TimeSynth: A Multipurpose Library for Synthetic Time Series Generation in Python” (available here) in 2017. Before going into the details of the…

The post Guide to TimeSynth – A Python Library For Synthetic Time Series Generation appeared first on Analytics India Magazine.

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