diff --git a/17_foundations.ipynb b/17_foundations.ipynb
index cb626de..ab98581 100644
--- a/17_foundations.ipynb
+++ b/17_foundations.ipynb
@@ -1137,7 +1137,7 @@
     "torch.einsum('bi,ij,bj->b', a, b, c)\n",
     "```\n",
     "\n",
-    "will return a vector of size `b` where the `k`-th coordinate is the sum of `a[k,i] b[i,j] c[k,j]`. This notation is particularly convenient when you have more dimensions because of batches. For example, if you have two batches of matrices and want compute the matrix product per batch, you would could this: \n",
+    "will return a vector of size `b` where the `k`-th coordinate is the sum of `a[k,i] b[i,j] c[k,j]`. This notation is particularly convenient when you have more dimensions because of batches. For example, if you have two batches of matrices and want to compute the matrix product per batch, you would could this: \n",
     "\n",
     "```python\n",
     "torch.einsum('bik,bkj->bij', a, b)\n",