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It is already here, actually. I do a lot of numerics related to simulating quantum computing hardware (which is just linear algebra of fairly large matrices).
by krastanov 9y ago
It is already here, actually.
I do a lot of numerics related to simulating quantum computing hardware (which is just linear algebra of fairly large matrices). I have used Theano to write some of the simulations, and there are plenty of projects on github that use TensorFlow as a backend for quantum-mechanical simulations.
Either way, I do not care much whether they call it a "deep learning" framework. It is still directly usable in all other types of linear algebra applications.
- amelius 9y agoThat's nice, until someone decides to prune certain functionality from future versions of the library because they are not needed for Deep Learning. Or until someone decides: hey, we can replace this floating point computation by a fixed point computation, because it doesn't matter for any kind of Deep Learning anyway.
- claytonjy 9y agoThe maintainers of major DL frameworks like TF and PyTorch intentionally target an ever-wider set of use-cases beyond deep learning, and would not chop out non-DL functionality. There's many cool non-DL projects using these libraries for fast computation like Pyro (PyTorch) & Greta (TensorFlow), and the community is starting to shift away from DL-specific vocabulary towards terms like "differentiable programming". Deep learning is just one use of this broader paradigm.
- nl 9y agoThat's really not how it works at all. Frameworks are layered, and the acceleratored tensor libraries are useful independently of the neural network architectures built on top of them. It's basically a numpy/scikit like relationship, except both come from the same project.