Discover @neelsoumya Open Source projects

Soumya Banerjee (@neelsoumya)

neelsoumya

Stars
130
Global Rank 171,488 (Top 6 %)
Followers 24
Following 46
Registered almost 12 years ago
Most used languages

R
28.2 %

Jupyter Notebook
17.9 %

Shell
15.4 %

Python
12.8 %

TeX
7.7 %

HTML
7.7 %

NetLogo 2.6 %

Haskell
2.6 %

PostScript 2.6 %

C++
2.6 %
Location 🇬🇧 United Kingdom
Country Total Rank 7,922
Country Ranking

NetLogo 18

PostScript 50

TeX
318

R
341

Haskell
901

Jupyter Notebook
1,514

Shell
1,571

HTML
2,257

C++
6,469

Python
8,603

dsSurvival

Survival functions for DataSHIELD. Package for building survival models, Cox proportional hazards models and Cox regression models in DataSHIELD.

dsSurvival_bookdown

A bookdown demonstrating how to build survival models using the dsSurvival package in DataSHIELD

special_topics_unconventional_AI

Special topics class on unconventional AI

teaching_reproducible_science_R

Material and notes for teaching reproducible science in R

dsSurvivalbookdown

A bookdown demonstrating how to build survival models using the dsSurvival package in DataSHIELD

public_open_source_data_science

A repository of open source data science projects for social good

Jupyter Notebook

dsSurvivalClient

Survival functions (client side) for DataSHIELD. Package for building survival models, Cox proportional hazards models and Cox regression models in DataSHIELD.

butterfly_detector

Basic tutorials and code for teaching deep learning and machine learning

Jupyter Notebook

join_pdf_shell_script

Shell script to join multiple PDFs using ghostscript

open_data

mathematics

A repository of free resources to teach yourself mathematics

teaching_resources

Compilation of teaching resources

outreach_ppi

Outreach and PPI resources for explaining AI to the public

survival_models

A repository of code and resources for survival models

meditator

Miscellaneous tools for meditation

bayesian_inference_linear_mixed_effect_models_pymc3

Example code to perform linear mixed effects regression in a Bayesian setting using the PyMc3 framework

ai_outreach

Resources for explaining AI to the public and outreach activities

ramanujan_number_generator

Generating Ramanujan cab numbers

complex_stories_explanations

Complex stories as explanations for machine learning models

Jupyter Notebook

very_basic_unix

very basic UNIX commands for newbies

paper_preprints

Preprints of papers by Soumya Banerjee

neelsoumya

About me (Soumya Banerjee)

pymc_examples

Examples and simple scripts for probabilistic programming using PyMC3

life_objective_viewpoint

An objective and complex systems viewpoint of life.

zenAI

pheatmap_example

Example scripts to generate heatmaps using the pheatmap library

perspective_epidemics_conflict_zones

Perspective on epidemics in conflict zones. Code, parameters and installation instructions.

programming_resources

Resources for learning and teaching programming.

essential_shell_scripts

Essential shell (bash) scripts

gaussian_process_tutorial

Tutorial and examples for Gaussian processes

dsMiscellaneous

Miscellaneous tools for use in DataSHIELD

basic_statistics

Repository for teaching basics of statistics for machine learning

generic_random_forest_regression

deep_dali

Computational art for dynamical systems

TuneableCounterfactuals

Jupyter Notebook

cricket_scores

public_talk_AI_India

public_talk_AI_India

miscellaneous_interests

Miscellaneous interest

working_with_domain_experts

How to work with domain experts in the field of AI

latexcommands

Repository of latex commands

mathematical_models

Repository for teaching mathematical models

misc_papers

Miscellaneous papers (other projects)

awards

Awards (feel good folder) public

psortb_parsing

Scripts to parse output from PSortB bioinformatics package.

basic_python

Python cheatsheet

patient_stratification_explainable_AI

Explainable AI applied to patient stratification

Jupyter Notebook

Ma_paintings_writing

Ma paintings writing (Kalyani Banerjee)

bioinformatics_resources

Resources on bioinformatics

haskell

project_ideas

Project ideas for students

visualization_lecture

visualization lecture

metaanalysis_models

Resources and code for meta-analysis models

accelerate

dsSurvival2bookdown

A bookdown demonstrating how to build survival models using the dsSurvival 2.0 package in DataSHIELD

bootstrap_example_python

A simple example for bootstrapping in python and R.

reading_list_journal_club

reading list journal club

dsCoxClient

Client for Cox functions in DataSHIELD

nlp_resources

Resources and teaching material for Natural Language Processing (NLP)

Jupyter Notebook

very_basic_R

very basic R for newbies

scmap_single_cell

practical_supervised_machine_learning

A practical in R for teaching supervised machine learning

public_supervised_machine_learning

A lecture on supervised machine learning

writing_productivity

Writing and productivity

lymph_node_inspired_algorithms

lymph node inspired algorithms

old_software

travel

forecasting_port_throughput

Forecasting port throughput

Jupyter Notebook

abm_old

essential_utilities_miscellaneous

Essential miscellaneous utilities

linear_mixed_effects_model_teaching

Teaching material for linear mixed effects models

ethical_AI

A repository of tools and resources for ethical AI

graphical-user-interface-for-solving-ordinary-differential-equations

This function solves a set of ordinary differential equations (ODEs) that represent a target cell limited model with a GUI. It takes as input the ODE model parameters and an input file containing experimental data. It plots the numerically integrated solution of the ODE and the sum of squared residuals between experimental data and model solution.

dsSurvival2_analysis

dsSurvival 2.0 survival curves sensitivity analysis code

getting-_started_data_science

Resources to get started in data science and teach yourself data science