diff --git a/docs/key-concepts.md b/docs/key-concepts.md
index b87808d14..daab2c9fd 100644
--- a/docs/key-concepts.md
+++ b/docs/key-concepts.md
@@ -23,13 +23,13 @@ This section briefly introduces some key concepts of the JAX package.
 ## JAX arrays ({class}`jax.Array`)
 
 The default array implementation in JAX is {class}`jax.Array`. In many ways it is similar to
-the {class}`numpy.ndarray` type that you may be familar with from the NumPy package, but it
+the {class}`numpy.ndarray` type that you may be familiar with from the NumPy package, but it
 has some important differences.
 
 ### Array creation
 
 We typically don't call the {class}`jax.Array` constructor directly, but rather create arrays via JAX API functions.
-For example, {mod}`jax.numpy` provides familar NumPy-style array construction functionality
+For example, {mod}`jax.numpy` provides familiar NumPy-style array construction functionality
 such as {func}`jax.numpy.zeros`, {func}`jax.numpy.linspace`, {func}`jax.numpy.arange`, etc.
 
 ```{code-cell}
@@ -147,10 +147,10 @@ jaxprs later in {ref}`jax-internals-jaxpr`.
 ## Pytrees
 
 JAX functions and transformations fundamentally operate on arrays, but in practice it is
-convenient to write code that work with collections of arrays: for example, a neural
+convenient to write code that works with collection of arrays: for example, a neural
 network might organize its parameters in a dictionary of arrays with meaningful keys.
 Rather than handle such structures on a case-by-case basis, JAX relies on the {term}`pytree`
-abstraction to treat such collections in a uniform matter.
+abstraction to treat such collections in a uniform manner.
 
 Here are some examples of objects that can be treated as pytrees:
 
diff --git a/docs/quickstart.md b/docs/quickstart.md
index e19cb33ea..77cbb9d46 100644
--- a/docs/quickstart.md
+++ b/docs/quickstart.md
@@ -16,7 +16,7 @@ kernelspec:
 
 <!--* freshness: { reviewed: '2024-06-13' } *-->
 
-**JAX a library for array-oriented numerical computation (*à la* [NumPy](https://numpy.org/)), with automatic differentiation and JIT compilation to enable high-performance machine learning research**.
+**JAX is a library for array-oriented numerical computation (*à la* [NumPy](https://numpy.org/)), with automatic differentiation and JIT compilation to enable high-performance machine learning research**.
 
 This document provides a quick overview of essential JAX features, so you can get started with JAX quickly:
 
@@ -88,8 +88,8 @@ _ = selu_jit(x)  # compiles on first call
 %timeit selu_jit(x).block_until_ready()
 ```
 
-The above timing represent execution on CPU, but the same code can be run on GPU or TPU,
-typically for an even greater speedup.
+The above timing represents execution on CPU, but the same code can be run on GPU or
+TPU, typically for an even greater speedup.
 
 For more on JIT compilation in JAX, check out {ref}`jit-compilation`.
 
@@ -183,7 +183,7 @@ print('Naively batched')
 %timeit naively_batched_apply_matrix(batched_x).block_until_ready()
 ```
 
-A programmer familiar with the the `jnp.dot` function might recognize that `apply_matrix` can
+A programmer familiar with the `jnp.dot` function might recognize that `apply_matrix` can
 be rewritten to avoid explicit looping, using the built-in batching semantics of `jnp.dot`:
 
 ```{code-cell}