diff --git a/notebooks/03-basic-gan.ipynb b/notebooks/03-basic-gan.ipynb
index d88a524285..a19153e133 100644
--- a/notebooks/03-basic-gan.ipynb
+++ b/notebooks/03-basic-gan.ipynb
@@ -37,7 +37,7 @@
         "How to train a GAN!\n",
         "\n",
         "Main takeaways:\n",
-        "1. Generator and discriminator are arbitraty PyTorch modules.\n",
+        "1. Generator and discriminator are arbitrary PyTorch modules.\n",
         "2. training_step does both the generator and discriminator training.\n",
         "\n",
         "---\n",
diff --git a/notebooks/05-trainer-flags-overview.ipynb b/notebooks/05-trainer-flags-overview.ipynb
index ad75bf739e..a070ce0362 100644
--- a/notebooks/05-trainer-flags-overview.ipynb
+++ b/notebooks/05-trainer-flags-overview.ipynb
@@ -222,7 +222,7 @@
         "# 2. Init Trainer\n",
         "#####################\n",
         "\n",
-        "# these 2 flags are explained in the later sections...but for short explaination:\n",
+        "# these 2 flags are explained in the later sections...but for short explanation:\n",
         "# - progress_bar_refresh_rate: limits refresh rate of tqdm progress bar so Colab doesn't freak out\n",
         "# - max_epochs: only run 2 epochs instead of default of 1000\n",
         "trainer = pl.Trainer(progress_bar_refresh_rate=20, max_epochs=2)\n",
@@ -542,7 +542,7 @@
       },
       "source": [
         "# run through only 25% of the test set each epoch\n",
-        "trainer = Trainer(limit_test_batches=0.25)\n",
+        "trainer = pl.Trainer(limit_test_batches=0.25)\n",
         "\n",
         "trainer.fit(model, train_loader, val_loader)"
       ],
@@ -742,7 +742,7 @@
         "id": "-mevgiy_hkip"
       },
       "source": [
-        "To avoid the preformace decrease you can also set `log_gpu_memory=min_max` to only log the min and max memory on the master node.\n"
+        "To avoid the performance decrease you can also set `log_gpu_memory=min_max` to only log the min and max memory on the master node.\n"
       ]
     },
     {
@@ -977,7 +977,7 @@
       "source": [
         "### DDP2\n",
         "\n",
-        "In certain cases, it’s advantageous to use ***all*** batches on the same machine, instead of a subset. For instance, in self-supervised learning, a common performance bosst comes from increasing the number of negative samples.\n",
+        "In certain cases, it’s advantageous to use ***all*** batches on the same machine, instead of a subset. For instance, in self-supervised learning, a common performance boost comes from increasing the number of negative samples.\n",
         "\n",
         "In this case, we can use DDP2 which behaves like DP in a machine and DDP across nodes. DDP2 does the following:\n",
         "\n",
@@ -1643,10 +1643,10 @@
         "1. Dumping the current state of the model and trainer\n",
         "\n",
         "2. Iteratively until convergence or maximum number of tries max_trials (default 25) has been reached:\n",
-        "* Call fit() method of trainer. This evaluates steps_per_trial (default 3) number of training steps. Each training step can trigger an OOM error if the tensors (training batch, weights, gradients ect.) allocated during the steps have a too large memory footprint.\n",
+        "* Call fit() method of trainer. This evaluates steps_per_trial (default 3) number of training steps. Each training step can trigger an OOM error if the tensors (training batch, weights, gradients etc.) allocated during the steps have a too large memory footprint.\n",
         "  * If an OOM error is encountered, decrease the batch size\n",
         "  * Else increase it.\n",
-        "* How much the batch size is increased/decreased is determined by the chosen stratrgy.\n",
+        "* How much the batch size is increased/decreased is determined by the chosen strategy.\n",
         "\n",
         "3. The found batch size is saved to model.hparams.batch_size\n",
         "\n",
@@ -1790,7 +1790,7 @@
         "trainer.fit(model)\n",
         "```\n",
         "\n",
-        "The figure produced by lr_finder.plot() should look something like the figure below. It is recommended to not pick the learning rate that achives the lowest loss, but instead something in the middle of the sharpest downward slope (red point). This is the point returned py lr_finder.suggestion().\n",
+        "The figure produced by lr_finder.plot() should look something like the figure below. It is recommended to not pick the learning rate that achieves the lowest loss, but instead something in the middle of the sharpest downward slope (red point). This is the point returned py lr_finder.suggestion().\n",
         "\n",
         "![image.png](data:image/png;base64,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)"
       ]