DiffSharp
ocaml-torch
| DiffSharp | ocaml-torch | |
|---|---|---|
| 4 | 1 | |
| 599 | 431 | |
| 0.0% | 0.5% | |
| 4.6 | 2.5 | |
| over 1 year ago | 11 months ago | |
| F# | OCaml | |
| BSD 2-clause "Simplified" License | Apache License 2.0 |
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DiffSharp
- OCaml's Wings for Machine Learning
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Why is F# code so robust and reliable?
More expressive nature of F# means you don't have that many files. C# is luckily and finally moving into that direction too. There was no reason for "one file per class" policy anyway, but it was still widely adopted historically.
Here's an example of a worst-case scenario (GUI frameworks have notoriously huge amount of code): https://github.com/fsprojects/Avalonia.FuncUI/blob/master/sr...
But realistically an average project would look closer to this instead: https://github.com/DiffSharp/DiffSharp/blob/dev/src/DiffShar...
Once you have enough files, it might be a good idea to factor out separate concerns into different projects.
- Automatic differentiation in a lot more than 38 lines of F#
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Neural Networks Fsharp
Yes. You can use TensorFlow.NET or DiffSharp.
ocaml-torch
What are some alternatives?
corgi - A neural network, and tensor dynamic automatic differentiation implementation for Rust.
lamp - deep learning and scientific computing framework with native CPU and GPU backend for the Scala programming language
hyperlearn - 2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
norse - Deep learning with spiking neural networks (SNNs) in PyTorch.
dfdx - Deep learning in Rust, with shape checked tensors and neural networks
DALI - A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.