typos + text format

Author: pritesh2000

03_pytorch_computer_vision.ipynb

@@ -532,7 +532,7 @@
     "![example input and output shapes of the fashionMNIST problem](https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/03-computer-vision-input-and-output-shapes.png)\n",
     "*Various problems will have various input and output shapes. But the premise remains: encode data into numbers, build a model to find patterns in those numbers, convert those patterns into something meaningful.*\n",
     "\n",
-    "If `color_channels=3`, the image comes in pixel values for red, green and blue (this is also known a the [RGB color model](https://en.wikipedia.org/wiki/RGB_color_model)).\n",
+    "If `color_channels=3`, the image comes in pixel values for red, green and blue (this is also known as the [RGB color model](https://en.wikipedia.org/wiki/RGB_color_model)).\n",
     "\n",
     "The order of our current tensor is often referred to as `CHW` (Color Channels, Height, Width).\n",
     "\n",
@@ -802,7 +802,7 @@
     "\n",
     "But I think coding a model in PyTorch would be faster.\n",
     "\n",
-    "> **Question:** Do you think the above data can be model with only straight (linear) lines? Or do you think you'd also need non-straight (non-linear) lines?"
+    "> **Question:** Do you think the above data can be modeled with only straight (linear) lines? Or do you think you'd also need non-straight (non-linear) lines?"
    ]
   },
   {
@@ -999,7 +999,7 @@
     "\n",
     "Our baseline will consist of two [`nn.Linear()`](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html) layers.\n",
     "\n",
-    "We've done this in a previous section but there's going to one slight difference.\n",
+    "We've done this in a previous section but there's going to be one slight difference.\n",
     "\n",
     "Because we're working with image data, we're going to use a different layer to start things off.\n",
     "\n",
@@ -1430,7 +1430,7 @@
     "            # 1. Forward pass\n",
     "            test_pred = model_0(X)\n",
     "           \n",
-    "            # 2. Calculate loss (accumatively)\n",
+    "            # 2. Calculate loss (accumulatively)\n",
     "            test_loss += loss_fn(test_pred, y) # accumulatively add up the loss per epoch\n",
     "\n",
     "            # 3. Calculate accuracy (preds need to be same as y_true)\n",
@@ -1578,7 +1578,7 @@
     "\n",
     "Now let's setup some [device-agnostic code](https://pytorch.org/docs/stable/notes/cuda.html#best-practices) for our models and data to run on GPU if it's available.\n",
     "\n",
-    "If you're running this notebook on Google Colab, and you don't a GPU turned on yet, it's now time to turn one on via `Runtime -> Change runtime type -> Hardware accelerator -> GPU`. If you do this, your runtime will likely reset and you'll have to run all of the cells above by going `Runtime -> Run before`."
+    "If you're running this notebook on Google Colab, and you don't have a GPU turned on yet, it's now time to turn one on via `Runtime -> Change runtime type -> Hardware accelerator -> GPU`. If you do this, your runtime will likely reset and you'll have to run all of the cells above by going `Runtime -> Run before`."
    ]
   },
   {
@@ -1855,7 +1855,7 @@
     "\n",
     "We'll do so inside another loop for each epoch.\n",
     "\n",
-    "That way for each epoch we're going a training and a testing step.\n",
+    "That way, for each epoch, we're going through a training step and a testing step.\n",
     "\n",
     "> **Note:** You can customize how often you do a testing step. Sometimes people do them every five epochs or 10 epochs or in our case, every epoch.\n",
     "\n",
@@ -1966,7 +1966,7 @@
     "\n",
     "> **Note:** The training time on CUDA vs CPU will depend largely on the quality of the CPU/GPU you're using. Read on for a more explained answer.\n",
     "\n",
-    "> **Question:** \"I used a a GPU but my model didn't train faster, why might that be?\"\n",
+    "> **Question:** \"I used a GPU but my model didn't train faster, why might that be?\"\n",
     ">\n",
     "> **Answer:** Well, one reason could be because your dataset and model are both so small (like the dataset and model we're working with) the benefits of using a GPU are outweighed by the time it actually takes to transfer the data there.\n",
     "> \n",