The article is exceptionally clear and well presented. We need more of this level of writing about machine learning. For me, it is most important to note what

The article is exceptionally clear and well presented. We need more of this level of writing about machine learning. For me, it is most important to note what the article touches only at the end. If you have good features and representation, a linear model may be enough. Real world data and tasks usually don't require knot untying. In my opinion, the model (e.g., neural net) should be kept simple, and the challenge is in finding the right features and representation. Coates & Ng (2012) wrote: Many algorithms are available to learn deep hierarchies of features from unlabeled data, especially images. In many cases, these algorithms involve multi-layered networks of features (e.g., neural net- works) that are sometimes tricky to train and tune and are difficult to scale up to many machines effectively. Recently, it has been found that K-means clustering can be used as a fast alternative training method. The main advantage of this approach is that it is very fast and easily implemented at large scale. On the other hand, employing this method in practice is not completely trivial: K-means has several limitations, and care must be taken to combine the right ingredients to get the system to work well. http://www.stanford.edu/~acoates/papers/coatesng_nntot2012.pdf http://www.stanford.edu/~acoates/papers/coatesng_nntot2012.p...