Comments I liked reading this informative article. I also loved the bibliography of this article and found it very relevant. IMHO, some questions that could be answered by the article to make it more insightful are as follows: Why can the functions in Recurrent networks be only a sigmoid or a tanh? How does RNN accommodate probability? BPTT also has the tendency of getting stuck in local minima. Why? Are there are any solutions to the problems with BPTT?
- Edit: The link to MarkovChains points to MarkovNetworks. It should point to http://wiki.ubc.ca/Course:CPSC522/Markov_Chains
- Edit: The link to Sigmoid function is a generic link to Sigmoid. It should point to https://en.wikipedia.org/wiki/Sigmoid_function
- Softmax could also be hyperlinked.
- The To Add section lists a link to a more detailed architecture. So rename the "To Add" section to "Further reading"
* The topic is relevant for the course: 5 * The writing is clear and the English is good: 5 * The page is written at an appropriate level for CPSC 522 students (where the students have diverse backgrounds): 5 * The formalism (definitions, mathematics) was well chosen to make the page easier to understand: 5 * The abstract is a concise and clear summary: 5 * There were appropriate (original) examples that helped make the topic clear: 5 * There was appropriate use of (pseudo-) code: 2 * It had a good coverage of representations, semantics, inference and learning (as appropriate for the topic): 4 * It is correct: 5 * It was neither too short nor too long for the topic: 5 * It was an appropriate unit for a page (it shouldn't be split into different topics or merged with another page): 5 * It links to appropriate other pages in the wiki: 5 * The references and links to external pages are well chosen: 5 * I would recommend this page to someone who wanted to find out about the topic: 4 * This page should be highlighted as an exemplary page for others to emulate: 4
If I was grading it out of 20, I would give it: 19