Optimize compute performance, simplify training language models, and access comprehensive training options.
Chainer
Quickly build models, experiment with neural networks, and leverage a library of ML algorithms and functions.
Chainer is a flexible framework for neural networks that serves as a bridge between algorithms and implementations of deep learning by supporting CUDA computation with minimal code requirements. It allows users to leverage GPU resources efficiently through a few lines of code while also running on multiple GPUs with little additional effort. The framework supports various network architectures including feed-forward nets, convnets, and recurrent networks for neural network development tasks. Currently the project is under maintenance phase but remains available for those working on specific deep learning implementations.
Similar tools
Same job, different trade-offs.
Accurately identifies objects, people, and places in media files, detects text and logos, and offers customizable results.
Create, deploy, and monitor predictive analytics models for structured and unstructured data in real-time.
Quickly develop deep learning models, train large networks efficiently, and scale up models with GPU, CPU, and cloud computing.