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@@ -299,7 +299,7 @@ An implementation of Kernel SHAP, a model agnostic method to estimate SHAP value
 
 - [**Census income classification with scikit-learn**](https://slundberg.github.io/shap/notebooks/Census%20income%20classification%20with%20scikit-learn.html) - Using the standard adult census income dataset, this notebook trains a k-nearest neighbors classifier using scikit-learn and then explains predictions using `shap`.
 
-- [**ImageNet VGG16 Model with Keras**](https://slundberg.github.io/shap/notebooks/ImageNet%20VGG16%20Model%20with%20Keras.html) - Explain the classic VGG16 convolutional nerual network's predictions for an image. This works by applying the model agnostic Kernel SHAP method to a super-pixel segmented image.
+- [**ImageNet VGG16 Model with Keras**](https://slundberg.github.io/shap/notebooks/ImageNet%20VGG16%20Model%20with%20Keras.html) - Explain the classic VGG16 convolutional neural network's predictions for an image. This works by applying the model agnostic Kernel SHAP method to a super-pixel segmented image.
 
 - [**Iris classification**](https://slundberg.github.io/shap/notebooks/Iris%20classification%20with%20scikit-learn.html) - A basic demonstration using the popular iris species dataset. It explains predictions from six different models in scikit-learn using `shap`.