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hyperparameter-optimization
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An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
python
data-science
machine-learning
deep-learning
neural-network
tensorflow
machine-learning-algorithms
pytorch
distributed
feature-extraction
hyperparameter-optimization
feature-engineering
nas
bayesian-optimization
automl
automated-machine-learning
model-compression
neural-architecture-search
deep-neural-network
automated-feature-engineering
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Updated
Sep 18, 2021 - Python
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
python
data-science
machine-learning
automation
random-forest
scikit-learn
model-selection
xgboost
hyperparameter-optimization
feature-engineering
automl
gradient-boosting
automated-machine-learning
parameter-tuning
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Jul 1, 2021 - Python
eddiebergman
commented
Jul 29, 2021
Building the doc fails for example 40_advanced/example_single_configurations on the current development branch
...
generating gallery for examples/40_advanced... [ 50%] example_debug_logging.py
Warning, treated as error:
/home/runner/work/auto-sklearn/auto-sklearn/examples/40_advanced/example_single_configu
nzw0301
commented
Sep 19, 2021
Optuna's tutorial is a good source to understand Optuna's functionality with concrete examples. However, some tutorial pages might not be mentioned in corresponding Optuna's API pages.
Description
Please a
cpoptic
commented
Mar 31, 2021
When running TabularPredictor.fit(), I encounter a BrokenPipeError for some reason.
What is causing this?
Could it be due to OOM error?
Fitting model: XGBoost ...
-34.1179 = Validation root_mean_squared_error score
10.58s = Training runtime
0.03s = Validation runtime
Fitting model: NeuralNetMXNet ...
-34.2849 = Validation root_mean_squared_error score
43.63s =
A curated list of automated machine learning papers, articles, tutorials, slides and projects
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Updated
Sep 10, 2021
Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization
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Dec 22, 2020 - Python
[UNMAINTAINED] Automated machine learning for analytics & production
python
data-science
machine-learning
deep-learning
analytics
tensorflow
scikit-learn
keras
artificial-intelligence
xgboost
hyperparameter-optimization
lightgbm
machine-learning-library
deeplearning
production-ready
feature-engineering
machine-learning-pipelines
automl
gradient-boosting
automated-machine-learning
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Feb 10, 2021 - Python
pplonski
commented
Jun 25, 2021
Details in discussion mljar/mljar-supervised#421
Determined: Deep Learning Training Platform
kubernetes
machine-learning
deep-learning
tensorflow
pytorch
hyperparameter-optimization
hyperparameter-tuning
hyperparameter-search
distributed-training
ml-infrastructure
ml-platform
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Updated
Sep 21, 2021 - Python
Hyperparameter Optimization for TensorFlow, Keras and PyTorch
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May 27, 2021 - Python
A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.
data-science
machine-learning
scikit-learn
hyperparameter-optimization
ensemble-learning
automated-machine-learning
stacked-ensembles
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Jun 6, 2018 - Python
A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.
reinforcement-learning
keras
mcts
hyperparameter-optimization
evolutionary-algorithms
nas
monte-carlo-tree-search
hyperparameter-tuning
automl
neural-architecture-search
nasnet
enas
autodl
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Sep 16, 2021 - Python
A collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
reinforcement-learning
optimization
openai-gym
hyperparameters
openai
gym
hyperparameter-optimization
rl
zoo
hyperparameter-tuning
hyperparameter-search
pybullet
stable-baselines
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Feb 6, 2021 - Python
Open
Grid search variant
6
SimonBlanke
commented
Mar 9, 2021
I think it would be useful to have a grid search optimizer in this package. But its implementation would probably be quite different from other ones (sklearn, ...).
The requirements are:
- The grid search has to stop after n_iter instead of searching the entire search space
- The positions should not be precalculated at the beginning of the optimization (i have concerns about memory load).
A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.
tensorflow
pytorch
hyperparameter-optimization
awesome-list
quantization
nas
automl
model-compression
neural-architecture-search
meta-learning
architecture-search
quantized-training
model-acceleration
automated-feature-engineering
quantized-neural-network
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Jun 19, 2021
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
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Aug 19, 2021 - Jupyter Notebook
Python library to easily log experiments and parallelize hyperparameter search for neural networks
data-science
machine-learning
caffe
deep-learning
chainer
tensorflow
keras
pytorch
neural-networks
hyperparameter-optimization
grid-search
random-search
caffe2
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Jan 17, 2021 - JavaScript
Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries
python
data-science
machine-learning
ai
deep-learning
neural-network
optimization
scikit-learn
sklearn
keras
ml
artificial-intelligence
xgboost
hyperparameter-optimization
experimentation
lightgbm
feature-engineering
hyperparameter-tuning
catboost
rgf
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Jan 20, 2021 - Python
Sequential Model-based Algorithm Configuration
configuration
hyperparameter-optimization
bayesian-optimization
hyperparameter-tuning
automl
automated-machine-learning
hyperparameter-search
bayesian-optimisation
algorithm-configuration
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Updated
Sep 13, 2021 - Python
Tuning hyperparams fast with Hyperband
machine-learning
hyperparameters
hyperparameter-optimization
hyperparameter-tuning
gradient-boosting-classifier
gradient-boosting
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Updated
Aug 15, 2018 - Python
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
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Sep 18, 2021 - Jupyter Notebook
bcyphers
commented
Jan 31, 2018
If enter_data() is called with the same train_path twice in a row and the data itself hasn't changed, a new Dataset does not need to be created.
We should add a column which stores some kind of hash of the actual data. When a Dataset would be created, if the metadata and data hash are exactly the same as an existing Dataset, nothing should be added to the ModelHub database and the existing
a distributed Hyperband implementation on Steroids
hyperparameter-optimization
bayesian-optimization
automl
automated-machine-learning
neural-architecture-search
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Jan 31, 2021 - Python
Experimental Global Optimization Algorithm
optimization
hyperparameter-optimization
global-optimization
bayesian-optimization
blackbox-optimization
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Updated
Jan 29, 2018 - Python
guillaume-chevalier
commented
Jun 28, 2021
Describe the bug
Code could be more conform to pep8 and so forth.
Expected behavior
Less code st
Hyperparameter optimization for PyTorch.
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Jun 7, 2018 - Python
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
reinforcement-learning
robotics
optimization
lab
openai
gym
hyperparameter-optimization
rl
sde
hyperparameter-tuning
hyperparameter-search
pybullet
stable-baselines
pybullet-environments
tuning-hyperparameters
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Sep 21, 2021 - Python
An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.
python
metadata
data-science
machine-learning
deep-learning
optimization
scikit-learn
parallel-computing
keras
pytorch
artificial-intelligence
xgboost
hyperparameter-optimization
feature-engineering
bayesian-optimization
automated-machine-learning
parameter-tuning
neural-architecture-search
meta-learning
meta-heuristics
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Updated
Sep 14, 2021 - Python
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From a slack message:
Hi, So I observed that if you deploy a deployment with more replicas than the available resources serve keeps trying to allocate them waiting for autoscaler.