diff --git a/Part 4/model/mnist.ipynb b/Part 4/model/mnist.ipynb index b6c58ed..74bad86 100644 --- a/Part 4/model/mnist.ipynb +++ b/Part 4/model/mnist.ipynb @@ -37,7 +37,7 @@ "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", @@ -45,7 +45,7 @@ "\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 @@ ] } ] -} \ No newline at end of file +}