tiny binary search function with comparators
binary search algorithm
binary search function
A simple npm package that provides a binary search function for a sorted array
Binary Search Trees
Different binary search tree implementations, including a self-balancing one (AVL)
Different binary search tree implementations, including a self-balancing one (AVL)
Better binary searching
Helper function to build binary assignment operator visitors
This npm package provides a binary search function for searching elements in a sorted array.
Unpack multibyte binary values from buffers
Gives binary search function
Unicode Trie data structure for fast character metadata lookup, ported from ICU
An addon to node.js that provides a binary search function that runs in native C++.
a small binary search function independent of container
An unsorted binary search function for unnested arrays
This is a binary search function that creted with JavaScript. This is for the purpose of learning how to create a npm package
An iteration of the Node.js core streams with a series of improvements
JavaScript implementation of the BSER Binary Serialization
Implement search on any static website.
reads a BMFont binary in a Buffer into a JSON object
A binary search function
This package provides a binary search function that can handle unsorted arrays by first sorting them.
A fork of `binary-search-tree` 0.2.x with upgraded dependencies from the Sails core team.
Set of functions to create, modify and search in a binary tree from a list.
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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