mazzottidr

mazzottidr's Stars

• oxford-cs-deepnlp-2017/lectures

  Oxford Deep NLP 2017 course

  15.7k1.2k193.6k

• cheat/cheat

  cheat allows you to create and view interactive cheatsheets on the command-line. It was designed to help remind *nix system administrators of options for commands that they use frequently, but not frequently enough to remember.

  Language:Go12.5k190235822

• gabrielecirulli/2048

  The source code for 2048

  Language:JavaScript12.3k56516317.1k

• snorkel-team/snorkel

  A system for quickly generating training data with weak supervision

  Language:Python5.8k166980856

• sferik/twitter-ruby

  A Ruby interface to the Twitter API.

  Language:Ruby4.6k1306791.3k

• elabftw/elabftw

  :notebook: eLabFTW is the most popular open source electronic lab notebook for research labs.

  Language:PHP1k592.4k221

• jokergoo/circlize

  Circular visualization in R

  Language:R97727384147

• arq5x/bedtools2

  bedtools - the swiss army knife for genome arithmetic

  Language:C94558837287

• rstudio/flexdashboard

  Easy interactive dashboards for R

  Language:JavaScript81947367300

• benfulcher/hctsa

  Highly comparative time-series analysis

  Language:MATLAB7454946319

• kogakure/gitweb-theme

  An alternative theme for gitweb, strongly inspired by GitHub

  Language:CSS551321594

• OxWearables/biobankAccelerometerAnalysis

  Extracting meaningful health information from large accelerometer datasets

  Language:Python1942312964

• faridcher/ml-course

  Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language

  Language:R1731511148

• statgen/pheweb

  A tool to build a website to browse hundreds or thousands of GWAS.

  Language:Python1621220067

• stephenturner/qqman

  An R package for creating Q-Q and manhattan plots from GWAS results

  Language:HTML162165992

• SMAPPNYU/smappR

  R tools for analysis of Twitter data

  Language:R119191461

• garethjns/Kaggle-EEG

  Seizure prediction from EEG data using machine learning. 3rd place solution for Kaggle/Uni Melbourne seizure prediction competition.

  Language:MATLAB10471828

• tpoisot/digitize

  A R package to extract data from scatterplots

  Language:R709511

• karthik/rDrop

  Programmatic interface to Dropbox

  Language:R65141517

• jimhester/fasta_utilities

  A collection of scripts developed to interact with fasta, fastq and sam/bam files.

  Language:Perl524420

• stephenturner/twitterchive

  Archive and analyze results from a Twitter search (**no longer maintained**)

  Language:R33719

• maxconway/SNFtool

  Similarity Network Fusion

  Language:R313015

• ialbert/pyblue-central

  Static website generator for sciencey people

  Language:Python30438

• trestletech/ShinyDash-Sample

  Example shiny app built on the ShinyDash package.

  Language:R238

• nsrr/spout

  Tested JSON Data Dictionaries. Turn your CSV data dictionary into a JSON repository. Collaborate with others to update the data dictionary in JSON format. Generate new Data Dictionary from the JSON repository. Test and validate your data dictionary using built-in tests, or add your own tests and validations.

  Language:Ruby1171054

• UrbsLab/ExSTraCS_ML_Pipeline_Binary_Notebook

  An rigorous, well documented machine learning analysis pipeline for binary classification datasets assembled as a Jupyter Notebook. Includes exploratory analysis, data processing, feature processing, ML modeling (9 algorithms, including the original ExSTraCS algorithm) with hyperparameter sweeps, visualizations, and statistical analysis. A comprehensive starting point to adapt to your own dataset an as an example of how to integrate a non-scikit-learn ML algorithm into a comparative pipeline.

  Language:Python10413

• UrbsLab/AutoMLPipe-BC

  An automated, rigorous, and largely scikit-learn based machine learning analysis pipeline for binary classification. Adopts current best practices to avoid bias, optimize performance, ensure replicatability, capture complex associations (e.g. interactions and heterogeneity), and enhance interpretability. Includes (1) exploratory analysis, (2) data cleaning, (3) partitioning, (4) scaling, (5) imputation, (6) filter-based feature selection, (7) collective feature selection, (8) modeling with 'optuna' hyperparameter optimization across 13 implemented ML algorithms (including three rule-based machine learning algorithms: ExSTraCS, XCS, and eLCS), (9) testing evaluations with 16 classification metrics, model feature importance estimation, (10) automatically saves all results, models, and publication-ready plots (including proposed composite feature importance plots), (11) non-parametric statistical comparisons across ML algorithms and analyzed datasets, and (12) automatically generated PDF summary reports.

  Language:Jupyter Notebook7201

• patilv/Popsenti

  Assessing popularity of two entities on twitter

  Language:R6306

• sachsmc/cosinor

  R package for estimation and prediction of the cosinor model for periodic data

  Language:R5472