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automatic-machine-learning
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Generalized and Efficient Blackbox Optimization System [SIGKDD'21].
distributed-systems
saas
constrained-optimization
multi-objective-optimization
bayesian-optimization
hyper-parameter-optimization
blackbox-optimization
automatic-machine-learning
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May 10, 2022 - Python
State-of-the art Automated Machine Learning python library for Tabular Data
python
data-science
machine-learning
sklearn
cross-validation
ml
model-selection
xgboost
hyperparameter-optimization
machine-learning-library
hyperparameter-tuning
optimisation
automl
stacking
auto-ml
machine-learning-models
automatic-machine-learning
data-science-projects
stacking-ensemble
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Mar 7, 2021 - Python
DeepArchitect: Automatically Designing and Training Deep Architectures
machine-learning
deep-learning
hyperparameter-optimization
auto-ml
neural-architecture-search
architecture-search
automatic-machine-learning
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Oct 1, 2019 - Python
A general, modular, and programmable architecture search framework
machine-learning
deep-neural-networks
deep-learning
tensorflow
pytorch
neural-networks
colab
hyperparameter-optimization
auto-ml
neural-architecture-search
architecture-search
automatic-machine-learning
colab-notebook
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Feb 10, 2022 - Python
【数据科学家系列课程】
product-design
data-science
automation
algorithms
robotics
machine-learning-algorithms
mathematics
design-patterns
artificial-intelligence
startup
mathematical-statistics
algorithm-challenges
algorithm-analysis
architectural-patterns
system-architecture
architecture-visualization
foundation-framework
algorithm-visualisation
architectures
automatic-machine-learning
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May 11, 2022 - Jupyter Notebook
Package: R Interface to AutoKeras
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Jan 16, 2021 - R
Generalized and Efficient Blackbox Optimization System.
constrained-optimization
multi-objective-optimization
bayesian-optimization
hyperparameter-tuning
blackbox-optimization
automatic-machine-learning
knobs-tuning
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Jan 6, 2022 - Python
An automatic machine learning system
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Apr 20, 2020 - Python
Comparison of automatic machine learning libraries
machine-learning
mljar-api-python
predictive-modeling
prediction-algorithm
prediction-model
automatic-machine-learning
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Dec 7, 2017 - Python
This repo holds the code, dataset, and running scripts for fast k-means evaluation
evaluation
pipeline-framework
dataset
pruning
spatial-analysis
k-means
algorithm-selection
lloyds
automatic-machine-learning
fast-kmeans
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Mar 31, 2021 - Java
Surrogarte modelling technique selector
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Nov 14, 2019 - Jupyter Notebook
Awesome papers on AutoML (Automatic Machine Learning)
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Jul 20, 2021
SKSurrogate is a suite of tools that implements surrogate optimization for expensive functions based on scikit-learn. The main purpose of SKSurrogate is to facilitate hyperparameter optimization for machine learning models and optimized pipeline design (AutoML).
machine-learning
optimization
machine
regression
model-selection
hyperparameter-optimization
classification
automl
eoa
automatic-machine-learning
surrogate-based-optimization
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Dec 28, 2020 - Python
Final Year Btech Face recognition Attendance System Project with code and Documents. Video Implementation with explanation too.
python
final
automatic
attendance
finalyearproject
semester
final-year-project
final-project
attendance-system
btech
attendance-management-system
automatic-machine-learning
mtech
btech-project
btechfinalyear
btechproject
btechprojects
mtech-project
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May 4, 2022 - Python
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Sep 4, 2019 - Jupyter Notebook
EZStacking is Jupyter notebook generator for machine learning
machine-learning
scikit-learn
exploratory-data-analysis
keras
seaborn
xgboost
modelling
notebook-generator
automatic-machine-learning
yellowbrick
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May 17, 2022 - Python
Experiments of AutoML/data science packages and solving Kaggle competition in Google Colaboratory
python
data-science
machine-learning
deep-learning
neural-network
jupyter-notebook
python3
kaggle
datascience
kaggle-competition
automl
automatic-machine-learning
colaboratory
colab-notebook
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Apr 8, 2022 - Jupyter Notebook
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In order to reduce overfitting, I would like to ask for a new parameter: "n_repetitions". This parameter sets the number of complete sets of folds to compute for repeated k-fold cross-validation.
Cross-validation example: