Fix typos in Sound Classifier user guide (#3020)

Author: lukereichold

userguide/sound_classifier/advanced-usage.md

@@ -35,11 +35,11 @@ This allows us to perform 5-fold cross validation more than twice as fast. For l
 
 
 #### Tune Custom Neural Network Configuration
-The [custom neural network](how-it-works.html#custom-neural-network-stage) used by the Sound Classifier is made up of a series of dense layers. Using more layers or more units in each layer can have a significant affect on accuracy. This will also affect the size of your model.
+The [custom neural network](how-it-works.html#custom-neural-network-stage) used by the Sound Classifier is made up of a series of dense layers. Using more layers or more units in each layer can have a significant effect on accuracy. This will also affect the size of your model.
 
 The `custom_layer_sizes` parameter allows you specify how many layers and the number of units in each layer. The default values for this parameter is `[100, 100]` which corresponds to two dense layers with a 100 units each.
 
-Using a smaller number of overall units will result in a smaller model, potentially with minimal affects on accuracy. If you have a large amount of training data, you should get better accuracy by using more layers and/or more units.
+Using a smaller number of overall units will result in a smaller model, potentially with minimal effects on accuracy. If you have a large amount of training data, you should get better accuracy by using more layers and/or more units.
 
 The code below tries several different neural network configurations and reports the validation accuracy for each one:
 

userguide/sound_classifier/how-it-works.md

@@ -1,10 +1,12 @@
-# How Does this Work?
+# How Does This Work?
 
 Training and making predictions for a sound classifier model is a three
 stage process:
-1 - Signal preprocessing
-2 - A pretrained neural network is used to extract deep features
-3 - A custom neural network is used to make the predictions
+
+1. Signal preprocessing
+2. A pretrained neural network is used to extract deep features
+3. A custom neural network is used to make the predictions
+
 Details below about each stage.
 
 ## Signal Preprocessing Pipeline Stage
@@ -31,7 +33,7 @@ input length depends on sample rate) and produces an array of shape
 
 ## VGGish Feature Extraction Stage
 VGGish is a pretrained [Convolutional Neural Network](https://en.wikipedia.org/wiki/Convolutional_neural_network) from Google,
-see [their paper](https://ai.google/research/pubs/pub45611) and [their GitHub page](https://github.com/tensorflow/models/tree/master/research/audioset) for more details. As the name suggests, the architecture of
+see [their paper](https://ai.google/research/pubs/pub45611) and [their GitHub page](https://github.com/tensorflow/models/tree/master/research/audioset/vggish) for more details. As the name suggests, the architecture of
 this network is inspired by the famous VGG networks used for image
 classification. The network consists of a series of convolution and
 activation layers, optionally followed by a max pooling layer.
@@ -42,7 +44,7 @@ last three layers of the original VGGish model. We use the widest
 layer, from the original network, as our input data for the final
 stage. This modified VGGish model outputs a double vector of length
 12,288. On non-Linux systems, the model has also been eight bit
-quantized, to reduce its size.
+quantized to reduce its size.
 
 ## Custom Neural Network Stage
 This is the only stage which is updated based on the input data.