Bayesian optimization with Gaussian processes for hyperparameter tuning
Optimize hyperparameters using grid/random/bayesian search
Configuration utilities for rl-js: Reinforcement Learning in JavaScript
Astermind Pro - Premium ML Toolkit with Advanced RAG, Reranking, Summarization, and Information Flow Analysis
Hydra-style hierarchical configuration composition with YAML support
Type definitions and constants for wlearn
The most comprehensive tabular data analysis package for Vue.js - Advanced AI, ML, Statistical Analysis, and Data Science capabilities
Visualization tools for TVMAI model training simulations
The implementation details of FOLD-R++ algorithm. The target of FOLD-R++ algorithm is to learn an answer set program for a binary classification task.
Self-learning energy forecasting with conformal prediction and swarm-based ensemble models
Self-contained RL trading bot with DQN, PER, and market simulation
adds Node to call openai api via openai npm package
MCP server combining systematic thinking, mental models, debugging approaches, and stochastic algorithms for comprehensive cognitive pattern support
Training and Evaluation AI Model Recommendation Engine for WebNN
ConvNetJS is a Javascript implementation of Neural networks.
70+ ML algorithms across 15 families with AutoML, SIMD acceleration, and zero dependencies. Rust/WASM, browser + Node.
Enterprise-grade development toolkit for AI Engineers, Blockchain Developers, and Full-stack Engineers
Bayesian optimization of tensorflow models
Rank fusion algorithms for hybrid search — RRF, ISR, CombMNZ, Borda, DBSF. Zero dependencies.
useAIML is a comprehensive collection of custom React hooks designed to simplify and enhance artificial intelligence and machine learning workflows within React applications. It provides hooks for data loading, preprocessing, feature engineering, model tr
Enhanced MCP server providing comprehensive access to the Unified Pantheon framework with auto-improvement capabilities - 72 demon-angel pairs, 7 supreme demons, 6 supreme angels, Gillis Preternatural Epiphenomenal Philosophy, dual technology patterns, Nu
Mathematical optimization in JavaScript.
Use Python's #1 machine learning library from Node.js
Autonomous ML research harness for Claude Code. The autoresearch loop as a formal protocol — iteratively trains, evaluates, and improves ML models with structured experiment tracking, convergence detection, immutable evaluation infrastructure, and safety
A high performance configuration system for Rust.
Deep reinforcement learning algorithms for rlevo (internal crate — use `rlevo` for the full API)
OptiRS Neural Architecture Search and hyperparameter optimization
A complete library to interact with Aiplatform (protocol v1)
A complete library to interact with Aiplatform (protocol v1beta1)
Serenade: Session-based recommender system
Hyperparameter optimization and ablation studies
Tree-of-Parzen-estimators hyperparameter optimization
Bayesian and population-based optimization library with an Optuna-like API for hyperparameter tuning and black-box optimization
Hyperparameter optimization and search space exploration
Machine Learning optimization module for SciRS2 (scirs2-optim)
High-performance Gradient Boosted Decision Tree engine for large-scale tabular data
Red Optuna is a hyperparameter optimization framework. You can optimize hyperparameter automatically.
GRYDRA v2.0 is a complete, modular Ruby library for building, training, and deploying neural networks. NEW in v2.0: - Complete modular architecture with 29 organized files - Keyword arguments API for better readability - Full implementations (no more "simplified" versions) - 8 loss functions (MSE, MAE, Huber, Cross-Entropy, Hinge, Log-Cosh, Quantile) - 5 optimizers (Adam, SGD, RMSprop, AdaGrad, AdamW) - 6 training callbacks (EarlyStopping, LearningRateScheduler, ReduceLROnPlateau, ModelCheckpoint, CSVLogger, ProgressBar) - Complete LSTM implementation with backpropagation - Complete 2D Convolutional layer with padding and stride - Real PCA with eigenvalue decomposition using Power Iteration - Multiple activation functions (Tanh, ReLU, Leaky ReLU, Sigmoid, Swish, GELU, Softmax) - Regularization (Dropout, L1, L2) - Weight initialization (Xavier, He) - Data normalization (Z-Score, Min-Max) - Comprehensive metrics (MSE, MAE, Accuracy, Precision, Recall, F1, Confusion Matrix, AUC-ROC) - Advanced training (mini-batch, early stopping, learning rate decay, validation split) - Cross-validation and hyperparameter search - Text processing (vocabulary, binary vectorization, TF-IDF) - Model persistence (save/load with Marshal) - Network visualization and gradient analysis - Simplified EasyNetwork interface - 100% backward compatibility with v1.x Perfect for machine learning projects, research, and education in Ruby.
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