Model Training
Showing 36 of 74 projects
A deep learning framework for research, development, and production with flexible Python API and C++ core.
A JavaScript application framework for machine learning and its engineering, designed for Web developers.
A modular toolkit for machine learning, natural language processing, and text generation with TensorFlow and PyTorch versions.
A high-performance, scalable LLM library and reference implementation written in pure Python/JAX for training on TPUs and GPUs.
A collection of models, callbacks, and datasets to extend PyTorch Lightning for applied AI/ML research and production.
A lightweight library providing PyTorch training tools and utilities to simplify and standardize training loops.
A neural network library for Elixir built on Nx, providing functional, model creation, and training APIs for deep learning.
A unified framework for implementing and training deep learning models on tabular data using PyTorch and PyTorch Lightning.
Elephas is a Keras extension for distributed deep learning on Apache Spark, enabling data-parallel training at scale.
An all-in-one framework for training state-of-the-art computer vision models, covering pretraining, fine-tuning, and distillation.
A foundational PyTorch library for training deep learning models, serving as the core engine for the OpenMMLab ecosystem.
A fast, modular PyTorch reference implementation for training and evaluating semantic segmentation models.
A modern, object-oriented machine learning framework for R, providing efficient building blocks for ML workflows.
A lightweight TensorFlow library for training and evaluating Generative Adversarial Networks (GANs).
A distributed platform for rapid deep learning application development with neural network engine and Hadoop integration.
A high-performance, concurrent distributed cache system built in Rust for low-latency, high-throughput workloads.
A JAX-based framework for training large language models with a focus on legibility, scalability, and reproducibility.
Build fully-functioning computer vision and object detection models with PyTorch in just 5 lines of code.
A peer-to-peer platform for secure, privacy-preserving, decentralized data science and federated learning.
A simplified Keras-like framework for PyTorch that reduces boilerplate code for training neural networks.
A JAX-based machine learning framework for configuring and training large-scale models with high efficiency on TPUs and GPUs.
A TypeScript machine learning library for the web and Node.js with a simple, consistent API.
A standalone reimplementation of TensorFlow for Ruby, supporting pure Ruby and OpenCL backends for machine learning.
A book teaching practical patterns for building scalable and reliable distributed machine learning systems using Kubernetes, TensorFlow, Kubeflow, and Argo Workflows.
A parallel deep learning framework written in modern Fortran for training and inference of dense, convolutional, and transformer networks.
A high-level machine learning library for Go with a Keras-like API, built on Gorgonia.
An easy-to-use C# deep learning library with support for multiple backends including TensorFlow, PyTorch, and CUDA/OpenCL.
A PyTorch framework for deep learning on point clouds, providing a modular and reproducible foundation for 3D vision tasks.
A PyTorch frontend for JAX that enables running PyTorch code on TPUs and provides seamless PyTorch-JAX interoperability.
A collection of optimization algorithms and logging utilities for Torch machine learning models.
.NET Standard bindings for Apache MXNet, providing C# developers with NumPy-compatible APIs for machine learning model development, training, and deployment.
A collection of scripts for training random forests and sparse filtering models on Kaggle datasets.
A collection of Jupyter notebooks for learning the neon deep learning framework through hands-on tutorials.
A Delphi/Pascal binding for TensorFlow and Keras that enables Pascal developers to build, train, and deploy machine learning models.
A Ruby gem providing high-performance gradient boosting with LightGBM for machine learning tasks.
A training pipeline to generate four distinct AI NPC personality weight bundles for a Zelda-style N64 game, enabling byte-level transformer inference on the console's CPU.
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