Results for neural-data-normalizer

Data to bits normalization helpers script to use with neural network.

HTTP header normalizer middleware for the middy framework

HTTP event normalizer middleware for the middy framework

JSON API response normalizer

Parse and normalize AWS events middleware for the middy framework

This is library **convert datasets of human data** into **arrays of bits** understandable for neurons (duh).

Broccoli plugin to remove `modules/` subdirectory from legacy ember-cli addons.

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Self-Optimizing Neural Architecture (SONA) for Claude Flow — adaptive learning, trajectory tracking, pattern reuse, 7 RL algorithms (PPO/A2C/DQN/Q-Learning/SARSA/Decision Transformer/Curiosity), Flash Attention, MoE routing, LoRA, EWC++ for continual lear

SynthCode - Synthesize any LLM into a production-grade AI agent. Battle-tested agentic patterns, model-agnostic, TypeScript-first.

Converts HTTP headers to their most common casing. (e.g. usEr-Agent -> User-Agent)

React Style Normalizer

Graph Neural Network capabilities for Ruvector - Node.js bindings

Normalize email addresses

Data normalization script to use with neural networks

Utility to normalize JSON:API data with ease

Complete WASM toolkit for edge AI: vector search, graph DB, neural networks, DAG workflows, SQL/SPARQL/Cypher, and ONNX inference - all running in browser

HAL JSON API response normalizer

JavaScript Unicode 8.0 Normalization - NFC, NFD, NFKC, NFKD. Read <http://unicode.org/reports/tr15/> UAX #15 Unicode Normalization Forms.

Provide data formatters (data model builder & json builder) to work with JSON API specification v1.0 in your JavaScript / TypeScript code

Deeply normalizes object keys (included nested objects and objects in arrays) to the format of your choosing.

High-performance neural trading system with complete NAPI API (178 functions), advanced CLI with interactive mode, GPU acceleration, real-time execution, multi-agent swarm coordination, neural networks, risk management, sports betting, syndicate collabora

Ethereum Name Service (ENS) Name Normalizer

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.