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A set of utils for faster development of GraphQL tools
Simple async batch with concurrency control and progress reporting.
Batch processing in JS
TensorFlow layers API in JavaScript
SAP Cloud SDK for AI is the official Software Development Kit (SDK) for **SAP AI Core**, **SAP Generative AI Hub**, and **Orchestration Service**.
AWS SDK for JavaScript Batch Client for Node.js, Browser and React Native
A set of utils for faster development of GraphQL tools
Manage a cluster of child processes
Specialized Promise Extensions
Library modules used by contentful batch utility CLI tools.
The batch processing package for the Powertools for AWS Lambda (TypeScript) library.
Takes an async iterator that emits things and emits them as fixed size batches
Filter Cypress tests using title or tags
A data loading utility to reduce requests to a backend via batching and caching.
[](https://buildwithfern.com?utm_source=github&utm_medium=github&utm_campaign=readme&utm_source=https%3A%2F%2Fgithub.com%2Fvoyage-ai%2Ftypescript-sdk) [iterable values as functions with concurrency control
A batch manager that will deduplicate and batch requests for a certain data type made within a window.
Reduce requests to backend services by batching calls and caching records.
DataParallel and distributed training utilities for torch.rb. Split batches across multiple GPUs 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.