How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
Distributed machine learning platform
The main features of samsung/veles are: Artificial Intelligence, Deep Learning Frameworks, Machine Learning and AI, Distributed Computing.
Projects with overlapping indexed features include: pytorch/pytorch — PyTorch is a machine learning framework centered on a GPU-ready tensor library that supports multi-dimensional array… tensorflow/tensorflow — TensorFlow is a comprehensive machine learning framework designed for the construction, training, and deployment of… apache/incubator-mxnet — Apache MXNet is a deep learning framework and distributed machine learning library designed for training and deploying… microsoft/cntk — CNTK is a deep learning toolkit used for the design, construction, and training of neural networks. It defines model… bvlc/caffe — Caffe is a high-performance deep learning framework designed for training and deploying deep neural networks. It… christoschristofidis/awesome-deep-learning — This project is a curated directory of resources, libraries, and frameworks designed to support the development,…
PyTorch is a machine learning framework centered on a GPU-ready tensor library that supports multi-dimensional array operations across both CPU and accelerator hardware. It provides a foundational infrastructure for mathematical computation and dynamic neural network construction, utilizing a tape-based automatic differentiation system that allows for flexible, non-static graph execution. The framework is designed for deep integration with Python, enabling natural usage alongside standard scientific computing ecosystems. It distinguishes itself through a comprehensive distributed training sui
Apache MXNet is a deep learning framework and distributed machine learning library designed for training and deploying neural networks across distributed systems, mobile devices, and hardware accelerators. It functions as a cross-platform runtime and a dynamic dataflow scheduler that optimizes neural network execution. The framework provides a multi-language API, enabling the development of machine learning models using Python, R, Julia, Scala, Go, and JavaScript. It supports high-performance model training and the scaling of workloads across multiple GPUs and machines. The system covers cap
CNTK is a deep learning toolkit used for the design, construction, and training of neural networks. It defines model architectures as computational graphs and optimizes network parameters using an automatic differentiation engine and stochastic gradient descent. The project emphasizes large scale model distribution, spreading training workloads across multiple hardware nodes and GPUs. It features specialized support for dynamic sequence handling, allowing filters to be convolved across both spatial and dynamic sequence axes to process data of variable lengths. The toolkit provides hardware-a
TensorFlow is a comprehensive machine learning framework designed for the construction, training, and deployment of complex mathematical models. It utilizes a graph-based execution model that represents operations as directed acyclic graphs, enabling automatic differentiation and efficient parallel processing. The system provides high-level interfaces for defining neural network architectures, alongside a robust engine for managing multidimensional array structures and tensor mathematics. The framework distinguishes itself through a scalable distributed runtime that orchestrates workloads acr