numpy-ml
Machine learning, in numpy
파일 탐색기
최종 버전 다운로드 (.zip)- a--bug-performance-issue.md
- PULL_REQUEST_TEMPLATE.md
- custom.css
- decision_tree.png
- gmm.png
- gmm_model.png
- hmm_model.png
- lda_model_smoothed.png
- lda_model_unsmoothed.png
- mlp_model.png
- conf.py
- index.rst
- make.bat
- Makefile
- numpy_ml.bandits.bandits.rst
- numpy_ml.bandits.policies.rst
- numpy_ml.bandits.rst
- numpy_ml.bandits.trainer.rst
- numpy_ml.factorization.factors.rst
- numpy_ml.factorization.rst
- numpy_ml.gmm.gmm.rst
- numpy_ml.gmm.rst
- numpy_ml.hmm.MultinomialHMM.rst
- numpy_ml.hmm.rst
- numpy_ml.lda.lda.rst
- numpy_ml.lda.rst
- numpy_ml.lda.smoothed_lda.rst
- numpy_ml.linear_models.lm.rst
- numpy_ml.linear_models.rst
- numpy_ml.neural_nets.activations.rst
- numpy_ml.neural_nets.initializers.rst
- numpy_ml.neural_nets.layers.rst
- numpy_ml.neural_nets.losses.rst
- numpy_ml.neural_nets.models.rst
- numpy_ml.neural_nets.modules.rst
- numpy_ml.neural_nets.optimizers.rst
- numpy_ml.neural_nets.rst
- numpy_ml.neural_nets.schedulers.rst
- numpy_ml.neural_nets.utils.rst
- numpy_ml.neural_nets.wrappers.rst
- numpy_ml.ngram.additive.rst
- numpy_ml.ngram.goodturing.rst
- numpy_ml.ngram.mle.rst
- numpy_ml.ngram.rst
- numpy_ml.nonparametric.gp.rst
- numpy_ml.nonparametric.kernel_regression.rst
- numpy_ml.nonparametric.knn.rst
- numpy_ml.nonparametric.rst
- numpy_ml.preprocessing.dsp.rst
- numpy_ml.preprocessing.general.rst
- numpy_ml.preprocessing.nlp.rst
- numpy_ml.preprocessing.rst
- numpy_ml.rl_models.agents.rst
- numpy_ml.rl_models.rl_utils.rst
- numpy_ml.rl_models.rst
- numpy_ml.rl_models.trainer.rst
- numpy_ml.trees.dt.rst
- numpy_ml.trees.gbdt.rst
- numpy_ml.trees.losses.rst
- numpy_ml.trees.rf.rst
- numpy_ml.trees.rst
- numpy_ml.utils.data_structures.rst
- numpy_ml.utils.distance_metrics.rst
- numpy_ml.utils.graphs.rst
- numpy_ml.utils.kernels.rst
- numpy_ml.utils.rst
- numpy_ml.utils.testing.rst
- numpy_ml.utils.windows.rst
- README.md
- requirements.txt
- comparison.png
- EpsilonGreedy.png
- LinUCB.png
- ThompsonSamplingBetaBinomial.png
- UCB1.png
- __init__.py
- bandits.py
- policies.py
- README.md
- trainer.py
- __init__.py
- factors.py
- README.md
- plot.png
- __init__.py
- gmm.py
- README.md
- plot.png
- __init__.py
- hmm.py
- README.md
- plot_unsmoothed.png
- __init__.py
- lda.py
- lda_smoothed.py
- README.md
- plot_bayes.png
- plot_logistic.png
- plot_regression.png
- __init__.py
- bayesian_regression.py
- glm.py
- linear_regression.py
- logistic.py
- naive_bayes.py
- README.md
- ridge.py
- plot.png
- __init__.py
- activations.py
- README.md
- __init__.py
- initializers.py
- README.md
- __init__.py
- layers.py
- README.md
- __init__.py
- losses.py
- README.md
- __init__.py
- README.md
- vae.py
- w2v.py
- wgan_gp.py
- __init__.py
- modules.py
- README.md
- __init__.py
- optimizers.py
- README.md
- plot.png
- __init__.py
- README.md
- schedulers.py
- __init__.py
- README.md
- utils.py
- __init__.py
- README.md
- wrappers.py
- __init__.py
- README.md
- add_smooth.png
- rank_probs.png
- __init__.py
- ngram.py
- README.md
- gp_dist.png
- knn_plots.png
- kr_plots.png
- __init__.py
- gp.py
- kernel_regression.py
- knn.py
- README.md
- bandit_plots.py
- gmm_plots.py
- hmm_plots.py
- lda_plots.py
- lm_plots.py
- ngram_plots.py
- nn_activations_plots.py
- nn_schedulers_plots.py
- nonparametric_plots.py
- rl_plots.py
- trees_plots.py
- __init__.py
- dsp.py
- general.py
- nlp.py
- README.md
- CrossEntropyAgent-CartPole-v1.png
- CrossEntropyAgent-LunarLander-v2.png
- DynaAgent-Taxi-v2.png
- MonteCarloAgent-Copy-v0.png
- TemporalDifferenceAgent-Taxi-v2.png
- __init__.py
- tiles3.py
- __init__.py
- agents.py
- README.md
- rl_utils.py
- trainer.py
- __init__.py
- nn_torch_models.py
- test_glm.py
- test_linear_regression.py
- test_naive_bayes.py
- test_ngram.py
- test_nn.py
- test_nn_activations.py
- test_nonparametric.py
- test_preprocessing.py
- test_trees.py
- test_utils.py
- plot.png
- __init__.py
- dt.py
- gbdt.py
- losses.py
- README.md
- rf.py
- __init__.py
- data_structures.py
- distance_metrics.py
- graphs.py
- kernels.py
- misc.py
- README.md
- testing.py
- windows.py
- __init__.py
- README.md
- .gitignore
- .readthedocs.yml
- CODE_OF_CONDUCT.md
- CONTRIBUTING.md
- LICENSE
- MANIFEST.in
- README.md
- requirements-dev.txt
- requirements-test.txt
- requirements.txt
- setup.py
- tox.ini
# 설치 가이드
1. 코드 내려받기
git clone https://github.com/ddbourgin/numpy-ml
깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.
cd numpy-ml
방금 내려받은 프로젝트 폴더 안으로 이동합니다.
// repository documentation
Was this content helpful?
(0 ratings)