Fix some typos in mnist.ipynb

Part 4/model/mnist.ipynb

@@ -37,15 +37,15 @@
     "In this notebook we'll be designing and creating a simple neural network to recognize handwritten numbers using the [MNIST](https://en.wikipedia.org/wiki/MNIST_database) dataset.\n",
     "Neural networks are powerful computing systems which are vaguley inspired by the neurons in our brains!\n",
     "\n",
-    "Neural networks are a sequence of matrix multiplications. So our goal in training one is to \"learn\" coefficients which make our network an accurate predictor of the data. However, there's a lot that goes into how we can best \"learn\" those coefficients, so we'll stick to a pretty simple implementation of a nerual net.\n",
+    "Neural networks are a sequence of matrix multiplications. So our goal in training one is to \"learn\" coefficients which make our network an accurate predictor of the data. However, there's a lot that goes into how we can best \"learn\" those coefficients, so we'll stick to a pretty simple implementation of a neural net.\n",
     "\n",
     "The contents of this notebook are a little outside of the scope of what our club does, but we'll do our best to give you a rundown of what the code does.\n",
     "\n",
     "We'll also have some links at the bottom if you're interested in reading more!\n",
     "\n",
     "## High Level Explanation\n",
     "\n",
-    "Here's a pretty common visualization of how a neural network looks like:\n",
+    "Here's a pretty common visualization of how a neural network looks:\n",
     "\n",
     "![[Neural network visualization]()](https://i.ibb.co/mzCxfFH/nn.png)\n",
     "\n",
@@ -82,7 +82,7 @@
     "\n",
     "## Setup\n",
     "\n",
-    "We'll be using PyTorch to run our nerual network. Machine Learning requires a lot of disgusting math, so we want to use libraries to do the heavy lifting for us whenever possible.\n",
+    "We'll be using PyTorch to run our neural network. Machine Learning requires a lot of disgusting math, so we want to use libraries to do the heavy lifting for us whenever possible.\n",
     "\n",
     "[PyTorch](https://pytorch.org/) and [TensorFlow](https://www.tensorflow.org/) are both great libraries which make machine learning a lot easier to do. They provide high-level APIs for creating, training, and evaluating models. There's no strong case for why we use PyTorch here, but we do."
    ]
@@ -403,4 +403,4 @@
    ]
   }
  ]
-}
+}