Node.js client for the Big Bang Entropy API.
A small implementation of `crypto.getRandomValues` for React Native. This is useful to polyfill for libraries like [uuid](https://www.npmjs.com/package/uuid) that depend on it.
```bash npm install @entropy-is/entropy-api npm run dev ```
Cryptographic key pairs for the XRP Ledger
Generate XRPL Accounts with a number-based secret: 8 chunks of 6 digits
Calculate the entropy of a password string, but fast!
Generate XRPL Accounts with a number-based secret: 8 chunks of 6 digits
Temporary file and directory creator
BIP39 mnemonic for key derivation schemes
Generate more entropy to combine with Node's crypto.rng or window.crypto
Exodus BIP39 module
Chi-squared distribution entropy.
[](https://github.com/paralleldrive/aidd)[](https://paralleldrive.com)
TypeScript definitions for fast-password-entropy
The tmp package with promises support and disposers.
Beta distribution differential entropy.
MetaMask example snap demonstrating the use of `snap_getEntropy`
Meteor's Random Package for Straight Node
Gamma distribution differential entropy.
A fast implementation of a fisher-yates shuffle that does not mutate the source array.
Cryptographic key pairs for the XRP Ledger
Pure javascript implementation of Bip32Ed25519, used for Cardano blockchain key pair.
BIP39 JavaScript implementation
  , pronounceable passwords, pattern-based generation, password strength analysis, entropy calculation, and breach checking via HaveIBeenPwned API.
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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