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
Amazon DSSTNE is a machine learning toolkit and sparse tensor network library designed for deep learning models with sparse inputs and outputs. It provides a model-parallel training framework and a GPU-accelerated sparse engine to support memory-intensive networks. The framework is specifically designed for recommendation system training and large-scale sparse learning. It enables the distribution of large weight matrices and embedding tables across multiple GPU devices to handle models that exceed the memory capacity of a single processor. The project covers a broad range of capabilities in
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
An extension to Torch7's nn package.
The main features of clementfarabet/lua---nnx are: Deep Learning Frameworks, General Machine Learning.
Open-source alternatives to clementfarabet/lua---nnx include: apache/incubator-mxnet — Apache MXNet is a deep learning framework and distributed machine learning library designed for training and deploying… bvlc/caffe — Caffe is a high-performance deep learning framework designed for training and deploying deep neural networks. It… amznlabs/amazon-dsstne — Amazon DSSTNE is a machine learning toolkit and sparse tensor network library designed for deep learning models with… andersbll/deeppy — Deep learning in Python. benedekrozemberczki/karateclub — Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020). catalyst-team/catalyst — Accelerated deep learning R&D.
