wassname/jaxtyping
1
0
Fork 0
mirror of https://github.com/wassname/jaxtyping.git synced 2026-09-09 11:24:55 +08:00
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'main' of https://github.com/google/jaxtyping

Browse Source
This commit is contained in:
Patrick Kidger
2023-06-14 10:42:32 -07:00
parent 926dc53856 edc34f14f8
commit d2aa9c1e8d
2 changed files with 23 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files
README.md
+9 -5
View File
@@ -41,15 +41,19 @@ Available at [https://docs.kidger.site/jaxtyping](https://docs.kidger.site/jaxty
## Finally
### See also: other tools in the JAX ecosystem
### See also: other libraries in the JAX ecosystem
Neural networks: [Equinox](https://github.com/patrick-kidger/equinox).
[Equinox](https://github.com/patrick-kidger/equinox): neural networks.
Numerical differential equation solvers: [Diffrax](https://github.com/patrick-kidger/diffrax).
[Optax](https://github.com/deepmind/optax): first-order gradient (SGD, Adam, ...) optimisers.
Computer vision models: [Eqxvision](https://github.com/paganpasta/eqxvision).
[Diffrax](https://github.com/patrick-kidger/diffrax): numerical differential equation solvers.
SymPy<->JAX conversion; train symbolic expressions via gradient descent: [sympy2jax](https://github.com/google/sympy2jax).
[Lineax](https://github.com/google/lineax): linear solvers and linear least squares.
[Eqxvision](https://github.com/paganpasta/eqxvision): computer vision models.
[sympy2jax](https://github.com/google/sympy2jax): SymPy<->JAX conversion; train symbolic expressions via gradient descent.
### Disclaimer
docs/index.md
+14
View File
@@ -40,3 +40,17 @@ def accepts_pytree_of_arrays(x: PyTree[Float[Array, "batch c1 c2"]]):
## Next steps
Have a read of the [Array annotations](./api/array.md) documentation on the left-hand bar!
## See also: other libraries in the JAX ecosystem
[Equinox](https://github.com/patrick-kidger/equinox): neural networks.
[Optax](https://github.com/deepmind/optax): first-order gradient (SGD, Adam, ...) optimisers.
[Diffrax](https://github.com/patrick-kidger/diffrax): numerical differential equation solvers.
[Lineax](https://github.com/google/lineax): linear solvers and linear least squares.
[Eqxvision](https://github.com/paganpasta/eqxvision): computer vision models.
[sympy2jax](https://github.com/google/sympy2jax): SymPy<->JAX conversion; train symbolic expressions via gradient descent.