added alt-text to tutorial-deep-learning-mnist (#100)

* added alt-text to tutorial-deep-learning-mnist

Co-authored-by: Ross Barnowski <[email protected]>
Co-authored-by: Melissa Weber Mendonça <[email protected]>

Author: 3 people

content/tutorial-deep-learning-on-mnist.md

@@ -19,7 +19,13 @@ Your deep learning model — one of the most basic artificial neural networks th
 
 Based on the image inputs and their labels ([supervised learning](https://en.wikipedia.org/wiki/Supervised_learning)), your neural network will be trained to learn their features using forward propagation and backpropagation ([reverse-mode](https://en.wikipedia.org/wiki/Automatic_differentiation#Reverse_accumulation) differentiation). The final output of the network is a vector of 10 scores — one for each handwritten digit image. You will also evaluate how good your model is at classifying the images on the test set.
 
-![Diagram showing operations detailed in this tutorial](_static/tutorial-deep-learning-on-mnist.png)
+![Diagram showing operations detailed in this tutorial (The input image
+is passed into a Hidden layer that creates a weighted sum of outputs.
+The weighted sum is passed to the Non-linearity, then regularization and
+into the output layer. The output layer creates a prediction which can
+then be compared to existing data. The errors are used to calculate the
+loss function and update weights in the hidden layer and output
+layer.)](_static/tutorial-deep-learning-on-mnist.png)
 
 This tutorial was adapted from the work by [Andrew Trask](https://github.com/iamtrask/Grokking-Deep-Learning) (with the author's permission).
 
@@ -165,6 +171,9 @@ for sample, ax in zip(rng.choice(x_train, size=num_examples, replace=False), axe
     ax.imshow(sample.reshape(28, 28), cmap='gray')
 ```
 
+_Above are five images taken from the MNIST training set. Various hand-drawn
+Arabic numerals are shown, with exact values chosen randomly with each run of the code._
+
 > **Note:** You can also visualize a sample image as an array by printing `x_train[59999]`. Here, `59999` is your 60,000th training image sample (`0` would be your first). Your output will be quite long and should contain an array of 8-bit integers:
 >
 >
@@ -334,7 +343,14 @@ Afterwards, you will construct the building blocks of a simple deep learning mod
 
 Here is a summary of the neural network model architecture and the training process:
 
-![Diagram showing operations detailed in this tutorial](_static/tutorial-deep-learning-on-mnist.png)
+
+![Diagram showing operations detailed in this tutorial (The input image
+is passed into a Hidden layer that creates a weighted sum of outputs.
+The weighted sum is passed to the Non-linearity, then regularization and
+into the output layer. The output layer creates a prediction which can
+then be compared to existing data. The errors are used to calculate the
+loss function and update weights in the hidden layer and output
+layer.)](_static/tutorial-deep-learning-on-mnist.png)
 
 - _The input layer_:
 
@@ -552,6 +568,10 @@ axes[1].set_xlabel("Epochs")
 plt.show()
 ```
 
+_The training and testing error is shown above in the left and right
+plots, respectively. As the number of Epochs increases, the total error
+decreases and the accuracy increases._
+
 The accuracy rates that your model reaches during training and testing may be somewhat plausible but you may also find the error rates to be quite high.
 
 To reduce the error during training and testing, you can consider changing the simple loss function to, for example, categorical [cross-entropy](https://en.wikipedia.org/wiki/Cross_entropy). Other possible solutions are discussed below.