javascript hyperparameters search
Optimize hyperparameters using grid/random/bayesian search
AI-guided LLM optimization. Install → Tell Claude 'Read .claude/agents/iris.md' → Claude becomes your optimization guide. DSPy prompts, Ax hyperparameters, local LLMs, federated learning. You talk, Iris handles the rest.
tfjs-node-grid-search (TNGS) is a grid search utility for TensorFLow.js in Node.js. Simply define ranges of hyperparameters. For every combination, TNGS will create a model, train it and test it using your data set. Results are logged and (optionally) wri
A high density zero-obstacle solver
AutoML engine for wlearn: search space sampling, random search, successive halving, ensemble construction
CLI benchmark for measuring and mitigating sycophancy in LLMs. Supports multi-provider execution, configurable judges, and long-running evaluation campaigns.
**Unlock the Full Potential of AI with Continuous Self-Improvement**
Effect-native black-box optimization for TypeScript
Bayesian optimization of tensorflow models
Meta Symbiosis - a metastrategy for iconomi
DHTI CLI
Official implementation of `DUSt3R: Geometric 3D Vision Made Easy` [[Project page](https://dust3r.europe.naverlabs.com/)], [[DUSt3R arxiv](https://arxiv.org/abs/2312.14132)]
Research intelligence that makes your AI coding agent smarter - one command setup
Neural Trader neural network training and prediction
Chatbot maker for HuggingFace Inference API and other AI API providers and backends.
Gradmotion CLI — 训练任务管理命令行工具，支持 Agent 集成
Lets you generate natural probably-unique IDs using english words.
A command line application for creating and managing OpenAI fine-tuning jobs.
Barnes-Hut implementations of t-SNE in wasm
The platform for all your Data Science projects
Construct AI & ML models with JSON using Typescript & Tensorflow
Regression models for Saltcorn
Scaffold AI research projects with staged workflows
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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