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LiYangHart / Hyperparameter-Optimization-of-Machine-Learning-Algorithms

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Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

machine-learning deep-learning random-forest optimization svm genetic-algorithm machine-learning-algorithms hyperparameter-optimization artificial-neural-networks grid-search tuning-parameters knn bayesian-optimization hyperparameter-tuning random-search particle-swarm-optimization hpo python-examples python-samples hyperband
  • Updated Apr 9, 2021
  • Jupyter Notebook
onepanel

onepanelio / onepanel

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The open and extensible integrated development environment (IDE) for computer vision with built-in modules for model building, automated labeling, data processing, model training, hyperparameter tuning and workflow orchestration.

workflow computer-vision annotation integrated-development-environment tensorflow ide pipelines pytorch tensorboard jupyterlab machinelearning deeplearning workflows hyperparameter-tuning aiops mlops cvat training-pipelines
  • Updated Apr 29, 2021
  • Go
Neuraxle

Neuraxio / Neuraxle

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Feature: ExecutionContext could inherit from some steps mixins like a TruncableStep, and ExecutionContext Services could inherit from some mixins as well.

guillaume-chevalier
guillaume-chevalier commented May 10, 2021

Is your feature request related to a problem? Please describe.
We may want to apply operations recursively on the context and its services using recursive dict keys such as "context__logger" when doing pipeline.apply(...) to apply things in the context as well. This also allows for nesting services one into another.

Describe the solution you'd like

  • `class BaseService(... some mix
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enhancement good first issue help wanted
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amanchadha / coursera-deep-learning-specialization

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Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models

deep-learning neural-network coursera recurrent-neural-networks neural-networks hyperparameter-optimization regularization convolutional-neural-networks neural-machine-translation coursera-machine-learning convolutional-neural-network hyperparameter-tuning andrew-ng coursera-assignment cnns rnns andrew-ng-course recurrent-neural-network neural-style-transfer coursera-specialization
  • Updated Apr 3, 2021
  • Jupyter Notebook
evalml

alteryx / evalml

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Highly null data check: separate thresholds for pct null cols vs rows

dsherry
dsherry commented May 13, 2021

Background
In #2222 @jeremyliweishih updated the highly null data check to warn if individual rows exceed a threshold percentage of nulls. Currently that threshold is the same as the one used for checking columns in the same manner.

Proposal
Let's have separate thresholds for pct null rows vs pct null columns. That'll make it easy to tinker with the behavior on real data, in case we decid

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enhancement good first issue
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