Update brainpy_transform_concept.ipynb

Author: ztqakita

docs/core_concept/brainpy_transform_concept.ipynb

@@ -260,7 +260,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the above example, ``Linear`` object has two ``Variable``: *W* and *b*. The ``net`` we defined is further composed of two ``Linear`` objects. We can except that four variables can be retrieved from it. "
+    "In the above example, ``Linear`` object has two ``Variable``: *W* and *b*. The ``net`` we defined is further composed of two ``Linear`` objects. We can expect that four variables can be retrieved from it."
    ]
   },
   {
@@ -486,7 +486,7 @@
    "metadata": {},
    "source": [
     "Note that, when using `to_object()`, we need to explicitly declare all `BrainPyObject` and `Variable` used in this Python function. \n",
-    "Due to the recersively retrieval property of `BrainPyObject`, we only need to specify the latest composition object. \n",
+    "Due to the recursive retrieval property of `BrainPyObject`, we only need to specify the latest composition object.\n",
     "\n",
     "In the above `loss` object, we do not need to specify two ``Linear`` object. Instead, we only need to give the top level object ``net`` into ``to_object()`` transform. \n",
     "\n",
@@ -512,7 +512,7 @@
    "metadata": {},
    "source": [
     "BrainPy object-oriented transformations are designed to work on ``BrainPyObject``. \n",
-    "These transforamtions inclue autograd ``brainpy.math.grad()`` and JIT ``brainpy.math.jit()``. "
+    "These transformations include autograd ``brainpy.math.grad()`` and JIT ``brainpy.math.jit()``."
    ]
   },
   {
@@ -653,4 +653,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
+}