Alternatives and similar repositories for documentation

Users that are interested in documentation are comparing it to the libraries listed below

• noahgift / or
  Operations Research Algorithms
  ☆17Updated last year
• axsaucedo / seldon-core
  Machine Learning Deployment for Kubernetes
  ☆18Updated last year
• freedomtowin / kernelml
  ☆21Updated last year
• ibm-cloud-architecture / refarch-data-ai-analytics
  ☆15Updated 5 years ago
• fastforwardlabs / structural-time-series
  Structural Time Series on US electricity demand data
  ☆22Updated 4 years ago
• tomasonjo / zeppelin-graph-algo
  Repository of Notebooks taken from https://neo4j.com/graph-algorithms-book/
  ☆26Updated 5 years ago
• fidelity / classitransformers
  A abstract text classification library using language models. Build your fine-tuned text classifier in 5 steps.
  ☆10Updated 4 years ago
• PipelineAI / notebooks
  Sample Notebooks for PipelineAI
  ☆44Updated 2 years ago
• h2oai / driverlessai-tutorials
  H2OAI Driverless AI Code Samples and Tutorials
  ☆37Updated 7 months ago
• navdeep-G / interpretable-ml
  Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
  ☆21Updated 2 years ago
• data-describe / awesome-data-science-models
  A few end to end examples that use data-describe
  ☆16Updated 2 years ago
• georgian-io-archive / automl_benchmark
  Distributed, large-scale, benchmarking framework for rigorous assessment of automatic machine learning repositories, projects, and librar…
  ☆30Updated 2 years ago
• weehyong / MLScore
  Know your ML Score based on Sculley's paper
  ☆34Updated 6 years ago
• Spratiher9 / SparkDataset
  Instant search for and access to many datasets in Pyspark.
  ☆34Updated 2 years ago
• natbusa / datafaucet
  Productivity Utilities for Data Science with Python Notebooks
  ☆6Updated 5 years ago
• duarteocarmo / automation-post
  📝 A blog post about report generation and automation in python
  ☆40Updated 5 years ago
• AlexIoannides / lime-interpretable-ml
  An example of how the LIME algorithm can be used to provide real-world insight into the decision processes of a 'black-box' machine learn…
  ☆15Updated 6 years ago
• unravelin / code-test-data-science
  Data Scientist code test
  ☆19Updated 4 years ago
• oreillymedia / Data_Analytics_with_Hadoop
  ☆16Updated 7 years ago
• alexander-n-thomas / nlp.spark.annotate
  notebooks for nlp-on-spark
  ☆13Updated 8 years ago
• augerai / a2ml
  Common API for all "second gen" AutoML APIs: Auger.AI, Google Cloud AutoML and Azure AutoML
  ☆41Updated 5 months ago
• simonjupp / importing-lifesci-data-into-neo4j
  ☆11Updated 6 years ago
• JohnSnowLabs / spark-nlp-streamlit
  Spark NLP for Streamlit
  ☆15Updated 3 years ago
• FavioVazquez / ds-optimus
  How to do data science with Optimus, Spark and Python.
  ☆19Updated 5 years ago
• noahgift / managed_ml_systems_and_iot
  Managed Machine Learning Systems and Internet of Things Live Lesson
  ☆40Updated 4 months ago
• bodywork-ml / ml-pipeline-engineering
  Best practices for engineering ML pipelines.
  ☆35Updated 2 years ago
• mdymczyk / iot-pipeline
  ☆15Updated 7 years ago
• the-deep-learners / nyc-ds-academy
  In-class exercises for Deep Learning course at NYC Data Science Academy
  ☆32Updated 7 years ago
• lopusz / pydata-berlin-2018
  ☆15Updated 6 years ago
• drivendataorg / drivendata-submission-validator
  Simple validator for submissions to DrivenData competitions
  ☆19Updated 6 years ago