ctongfei/nexus

Experimental tensor-typed deep learning

37
/ 100
Emerging

This project helps deep learning engineers and researchers build neural networks with greater confidence and fewer runtime errors. By using Scala's static type system, it allows you to define the meaning of tensor axes in your code, preventing common dimension and shape-related bugs during compilation. You provide neural network architecture specifications in Scala, and the system ensures your tensor operations are dimensionally correct before the code even runs.

258 stars. No commits in the last 6 months.

Use this if you are a deep learning developer frustrated by debugging `TypeError`s or spending significant time ensuring tensor axes and dimensions are correctly aligned in frameworks like PyTorch or TensorFlow, and you're comfortable with Scala.

Not ideal if you prefer dynamic languages like Python for deep learning, are looking for a highly optimized production-ready framework with extensive community support, or are not familiar with Scala.

deep-learning-engineering neural-network-development machine-learning-research static-analysis scala-development
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 11 / 25

How are scores calculated?

Stars

258

Forks

16

Language

Scala

License

MIT

Last pushed

Sep 12, 2019

Commits (30d)

0

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