Cost function neural networks

Thoughts on the Stanford Online Machine Learning course
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This week is the last week of the Stanford Machine Learning course (, so here is my basic review of the course. Overall, the quality is quite good. The lectures are easily the most valuable part, as Andrew Ng is very organized and provides insightful explanations over the approaches and algorithms.

The homework problems use Octave, a mostly clone version of Matlab (including down to language spec), so it’s pretty easy to pick up for most math-inclined people.

The homework assignments themselves were mildly… disappointing? I’m not sure that’s the right word, but I feel they were really lacking in improving understanding of the primary concept at hand. At the start of the course, the homework assignments are concerned with implementing cost functions for linear regression and neural networks. At the end of the course, the homework assignments are concerned with implementing cost functions for k-means clustering – so it gets fairly repetitive, and doesn’t really make you aware of the steps involved with the different algorithms. Most of the supporting code that goes into the algorithms is written for you by the course team. Let’s take a look at what I mean.

In the following example, the course walks you through a collaborative filtering example to recommend movies that you might enjoy based on previous ratings. There are a number of steps that go into it:

Source: Brett Walenz

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