RoCS-Client SDK (Javascript/Typescript) For Fourier
Yet another one of those Reusable Component repos...
Pi extension package for ontology inspection, routing, and change workflows backed by ROCS.
Cognitive-driven multi-agent orchestration for society.db, prompt-vault, and agent-kernel
Tweets custom string on pressing button on LittleBits
Rust OSB client
Rate Of Change (ROC) Implementation In Rust
Command line rust documentation searching in the style of godoc
A Rust-based CLI tool for automatic SSL certificate renewal
Comprehensive evaluation metrics for ToRSh - powered by SciRS2
Machine Learning evaluation metrics module for SciRS2 (scirs2-metrics)
Evaluation metrics for sklears: accuracy, precision, recall, F1, ROC-AUC, etc.
Receiver Operating Characteristic (ROC) and Precision-Recall curve (PR) computation
Parachain testnet runtime for FRAME Contracts pallet.
Evaluation metrics for machine learning
A Rust implementation of the H-Measure for assessing binary classifiers.
A high-performance modification detection tool in Rust
Receiver operator characteristic (ROC)
Collection of Ruby classes wrapping the Redis data structures
Dead-simple ROC analysis in Ruby.
gnuplot wrapper for ruby, especially for plotting roc curves into svg files
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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