Modeule for trainning
Treinamento de criação de pacotes NPM.
A command line utility to help Cult of Bits partners develop with higher speed and reusing common code and best practices.
My first npm package
local tfjs models assets
Simple Lib
This is a test project for wind-mwa
App for playing random notes on a music sheet for sight reading training. Fully configurable.
Training and fine-tuning utilities for BitNet models
Training loops, loss composition, and optimization schedules for TensorLogic
A composable, deterministic text data pipeline for ML. Ingest, denoise, chunk, split, and sample multi-source corpora into reproducible training triplets.
A multi-modal neural network focused on maternal health predictions
Comprehensive training infrastructure for neural forecasting models
FFI-free Rust implementation of LIBSVM-compatible SVM training and prediction
Comprehensive neural network library with dataset loading, batch normalization, 9 activation functions, 5 loss functions, multiple optimizers, regularization, and clean async-first API
Training infrastructure for TrustformeRS
RL-based article extraction from HTML using Deep Q-Networks and heuristic fallback
RL-based article extraction from HTML using Deep Q-Networks and heuristic fallback
floDl — a flow-graph deep learning framework built on libtorch
Training loop utilities for JEPA models
Provides a transport to communicate easily with RESTful APIs.
Bullet Train
Train of fools
A Train "transport" plugin for Chef Inspec that allows testing of all Kubernetes API resources
Allows applications using Train to speak to AWS; handles authentication, cacheing, and SDK dependency management.
Allows applications using Train to speak to Habitat.
Allows applictaions using Train to speak to Windows using Remote Management; handles authentication, cacheing, and SDK dependency management.
Bullet Train Themes
API capabilities for apps built with Bullet Train framework
Bullet Train Fields
Bullet Train Sortable
Bullet Train Integrations
No description provided.
No description provided.