fix small typos in docs

docs/sharded-computation.ipynb

@@ -60,7 +60,7 @@
     "\n",
     "Key to all of the distributed computation approaches below is the concept of *data sharding*, which describes how data is laid out on the available devices.\n",
     "\n",
-    "How can JAX can understand how the data is laid out across devices? JAX's datatype, the {class}`jax.Array` immutable array data structure, represents arrays with physical storage spanning one or multiple devices, and helps make parallelism a core feature of JAX.  The {class}`jax.Array` object is designed with distributed data and computation in mind. Every `jax.Array` has an associated {mod}`jax.sharding.Sharding` object, which describes which shard of the global data is required by each global device. When you create a {class}`jax.Array` from scratch, you also need to create its `Sharding`.\n",
+    "How can JAX understand how the data is laid out across devices? JAX's datatype, the {class}`jax.Array` immutable array data structure, represents arrays with physical storage spanning one or multiple devices, and helps make parallelism a core feature of JAX.  The {class}`jax.Array` object is designed with distributed data and computation in mind. Every `jax.Array` has an associated {mod}`jax.sharding.Sharding` object, which describes which shard of the global data is required by each global device. When you create a {class}`jax.Array` from scratch, you also need to create its `Sharding`.\n",
     "\n",
     "In the simplest cases, arrays are sharded on a single device, as demonstrated below:"
    ]

docs/sharded-computation.md

@@ -39,7 +39,7 @@ jax.devices()
 
 Key to all of the distributed computation approaches below is the concept of *data sharding*, which describes how data is laid out on the available devices.
 
-How can JAX can understand how the data is laid out across devices? JAX's datatype, the {class}`jax.Array` immutable array data structure, represents arrays with physical storage spanning one or multiple devices, and helps make parallelism a core feature of JAX.  The {class}`jax.Array` object is designed with distributed data and computation in mind. Every `jax.Array` has an associated {mod}`jax.sharding.Sharding` object, which describes which shard of the global data is required by each global device. When you create a {class}`jax.Array` from scratch, you also need to create its `Sharding`.
+How can JAX understand how the data is laid out across devices? JAX's datatype, the {class}`jax.Array` immutable array data structure, represents arrays with physical storage spanning one or multiple devices, and helps make parallelism a core feature of JAX.  The {class}`jax.Array` object is designed with distributed data and computation in mind. Every `jax.Array` has an associated {mod}`jax.sharding.Sharding` object, which describes which shard of the global data is required by each global device. When you create a {class}`jax.Array` from scratch, you also need to create its `Sharding`.
 
 In the simplest cases, arrays are sharded on a single device, as demonstrated below:
 

docs/stateful-computations.md

@@ -144,7 +144,7 @@ This is because, like the strategy we just applied, object-oriented programming
 
 In our case, the `CounterV2` class is nothing more than a namespace bringing all the functions that use `CounterState` into one location. Exercise for the reader: do you think it makes sense to keep it as a class?
 
-Incidentally, you've already seen an example of this strategy in the JAX pseudo-randomness API, {mod}`jax.random`, shown in the :ref:`pseudorandom-numbers` section.
+Incidentally, you've already seen an example of this strategy in the JAX pseudo-randomness API, {mod}`jax.random`, shown in the {ref}`pseudorandom-numbers` section.
 Unlike Numpy, which manages random state using implicitly updated stateful classes, JAX requires the programmer to work directly with the random generator state -- the PRNG key.