Reverse Dependencies of dask-ml

The following projects have a declared dependency on dask-ml:

• bciavm — bciAVM is a machine learning pipeline used to predict property prices.
• bob.bio.base — Tools for running biometric recognition experiments
• bob.bio.face — Tools for running face recognition experiments
• bob.bio.video — Run biometric recognition algorithms on videos
• bob.learn.em — Bindings for EM machines and trainers of Bob
• bob.pad.face — Implements tools for spoofing or presentation attack detection in face biometrics
• bob.pipelines — Tools to build robust and extensible pipelines
• CIMLA — Counterfactual Inference by Machine Learning and Attribution Models
• classification-text-email — compiled packages
• climaticai — climaticai is a library that builds, optimizes, and evaluates machine learning pipelines
• coiled-runtime — Simple and fast way to get started with Dask
• dask-quik — function to make working in dask_cudf and dask quik-er
• dea-tools — Functions and algorithms for analysing Digital Earth Australia data.
• deafrica-tools — Functions and algorithms for analysing Digital Earth Africa data.
• dev-aa-test-1 — A Hy library that provides a Lispy functional interface by wrapping Python's popular data libraries, such as Pandas and Matplotlib.
• divina — Package for causal, scalable forecasting
• email-txt-classification — compiled packages
• eugene-tools — Elucidating the Utility of Genomic Elements with Neural Nets
• flowi — no summary
• gam — Global Explanations for Deep Neural Networks
• gators — Model building and model scoring library
• happy-learning — Toolbox for reinforced developing of machine learning models (as proof-of-concept)
• hyfive — A Hy library that provides a Lispy functional interface by wrapping Python's popular data libraries, such as Pandas and Matplotlib.
• hypergbm — A full pipeline AutoML tool integrated various GBM models
• hypernets — An General Automated Machine Learning Framework
• incremental-trees — Sklearn forests with partial fits
• JLpyUtils — General utilities to streamline data science and machine learning routines in python
• leapy — Real-time inference pipelines
• ml4chem — Machine learning for chemistry and materials.
• modelcreator — Machine Learning package for quick fast model generation and comparison
• motrainer — Parallel Training Measurement Operators (MO) for Data Assimilation (DA) Applications
• myylearn — An General Automated Machine Learning Framework
• neural-admixture — Rapid population clustering with autoencoders
• nocode-autonn — An AutoML framework for deep learning
• pyDSlib — General utilities to streamline data science and machine learning routines in python
• pyoptimus — Optimus is the missing framework for cleaning and pre-processing data in a distributed fashion.
• quantile-data-kit — An internal Quantile development kit for making working with data easier
• salmon-triplets — Efficient crowdsourcing for ordinal embeddings
• scanpy — Single-Cell Analysis in Python.
• sgkit — Statistical genetics toolkit
• soccer-xg — Train and analyse xG models on soccer event stream data
• tabular-toolbox — A library of extension and helper modules for tabular data base on python's machine learning frameworks.
• tpot — Tree-based Pipeline Optimization Tool
• TPOT-SH — Tree-based Pipeline Optimization Tool - Successive Halving
• TPOT2 — Tree-based Pipeline Optimization Tool
• VASPsol — A VASPsol python helper package to simplify calculations and analysis
• wxyz-notebooks — notebook demos for experimental Jupyter widgets