Skill
Chainer
Data Science, Analytics and AI/ML
Chainer is an open-source deep learning framework originally developed by Preferred Networks in Japan, notable for pioneering the 'define-by-run' approach where the computational graph is built dynamically as operations execute. It was used by researchers for building and training neural networks in Python before development shifted toward PyTorch, which adopted a similar dynamic-graph philosophy. Its concepts influenced the design of later deep learning libraries.
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