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docs update

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Awni Hannun
2024-01-03 20:14:05 -08:00
committed by CircleCI Docs
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commit f1dfa257d2
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docs/build/html/_sources/index.rst vendored
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@@ -19,7 +19,7 @@ The main differences between MLX and NumPy are:
The design of MLX is inspired by frameworks like `PyTorch
<https://pytorch.org/>`_, `Jax <https://github.com/google/jax>`_, and
`ArrayFire <https://arrayfire.org/>`_. A noteable difference from these
`ArrayFire <https://arrayfire.org/>`_. A notable difference from these
frameworks and MLX is the *unified memory model*. Arrays in MLX live in shared
memory. Operations on MLX arrays can be performed on any of the supported
device types without performing data copies. Currently supported device types
@@ -57,6 +57,7 @@ are the CPU and GPU.
python/random
python/transforms
python/fft
python/linalg
python/nn
python/optimizers
python/tree_utils