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Curated list of libraries for a faster machine learning workflow
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README.md

README.md

toolbox

Curated libraries for a faster workflow

Phase: Data

Data Annotation

Datasets

Importing Data

Data Augmentation

Phase: Exploration

Data Preparation

Notebook Exploration

  • View Jupyter notebooks through CLI: nbdime
  • Parametrize notebooks: papermill
  • Access notebooks programatically: nbformat
  • Convert notebooks to other formats: nbconvert
  • Extra utilities not present in frameworks: mlxtend
  • Maps in notebooks: ipyleaflet

Phase: Feature Engineering

Feature Generation

Phase: Modeling

Model Selection

NLP

Speech Recognition

RecSys

  • Factorization machines (FM), and field-aware factorization machines (FFM): xlearn
  • Scikit-learn like API: surprise
  • Recommendation System in Pytorch: CaseRecommender
  • Apriori algorithm: apyori

Computer Vision

Timeseries

Framework extensions

Phase: Validation

Model Training Monitoring

Interpretability

Phase: Optimization

Hyperparameter Optimization

Visualization

Phase: Production

Model Serialization

Scalability

Bechmark

API

Dashboard

Adversarial testing

Python libraries

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