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h2oai / h2o-3

Star 7.3k

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

python java data-science machine-learning opensource r big-data spark deep-learning hadoop random-forest gpu naive-bayes h2o distributed pca gbm ensemble-learning automl h2o-automl
  • Updated Oct 4, 2025
  • Jupyter Notebook

BoltzmannEntropy / interviews.ai

Star 4.7k

It is my belief that you, the postgraduate students and job-seekers for whom the book is primarily meant will benefit from reading it; however, it is my hope that even the most experienced researchers will find it fascinating as well.

python data-science machine-learning deep-learning information-theory jobs pytorch autograd artificial-intelligence feature-extraction ensemble-learning logistic-regression convolutional-neural-networks bayesian-statistics loss-functions interview-preparation pytorch-tutorial graduate-school jax
  • Updated Aug 22, 2025
awesome-decision-tree-papers

benedekrozemberczki / awesome-decision-tree-papers

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Star 2.4k

A collection of research papers on decision, classification and regression trees with implementations.

classifier machine-learning random-forest statistical-learning xgboost lightgbm gradient-boosting-machine ensemble-learning cart decision-tree tree-ensemble decision-tree-classifier gradient-boosting classification-trees decision-tree-learning decision-tree-model classification-model catboost regression-tree machine-learning-research
  • Updated Mar 16, 2024
  • Python

shubhomoydas / ad_examples

Star 864

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convol…

streaming timeseries time-series lstm generative-adversarial-network gan rnn autoencoder ensemble-learning trees active-learning concept-drift graph-convolutional-networks interpretability anomaly-detection adversarial-attacks explaination anogan unsuperivsed nettack
  • Updated May 22, 2024
  • Python

EnnengYang / Awesome-Model-Merging-Methods-Theories-Applications

Star 551

Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. arXiv:2408.07666.

generative-model ensemble-learning transfer-learning zero-shot-learning robustness multi-task-learning attack-defense generalization meta-learning model-fusion federated-learning continual-learning few-shot-learning diffusion-models multi-domain-learning foundation-models large-language-models llms model-merging knowledge-fusion
  • Updated Oct 2, 2025

Western-OC2-Lab / Intrusion-Detection-System-Using-Machine-Learning

Star 531

Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)

machine-learning random-forest xgboost hyperparameter-optimization intrusion-detection lightgbm ensemble-learning kmeans autonomous-vehicles bayesian-optimization cyber-security decision-tree hpo network-security stacking intrusion-detection-system python-examples catboost cicids2017
  • Updated Aug 6, 2025
  • Jupyter Notebook

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