{"action":"opened","issue":{"url":"https://api.github.com/repos/nstrayer/activity_detection_post/issues/3","repository_url":"https://api.github.com/repos/nstrayer/activity_detection_post","labels_url":"https://api.github.com/repos/nstrayer/activity_detection_post/issues/3/labels{/name}","comments_url":"https://api.github.com/repos/nstrayer/activity_detection_post/issues/3/comments","events_url":"https://api.github.com/repos/nstrayer/activity_detection_post/issues/3/events","html_url":"https://github.com/nstrayer/activity_detection_post/issues/3","id":339331725,"node_id":"MDU6SXNzdWUzMzkzMzE3MjU=","number":3,"title":"comments","user":{"login":"skeydan","id":469371,"node_id":"MDQ6VXNlcjQ2OTM3MQ==","avatar_url":"https://avatars1.githubusercontent.com/u/469371?v=4","gravatar_id":"","url":"https://api.github.com/users/skeydan","html_url":"https://github.com/skeydan","followers_url":"https://api.github.com/users/skeydan/followers","following_url":"https://api.github.com/users/skeydan/following{/other_user}","gists_url":"https://api.github.com/users/skeydan/gists{/gist_id}","starred_url":"https://api.github.com/users/skeydan/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/skeydan/subscriptions","organizations_url":"https://api.github.com/users/skeydan/orgs","repos_url":"https://api.github.com/users/skeydan/repos","events_url":"https://api.github.com/users/skeydan/events{/privacy}","received_events_url":"https://api.github.com/users/skeydan/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-07-09T07:19:34Z","updated_at":"2018-07-09T07:19:34Z","closed_at":null,"author_association":"NONE","body":"Awesome post - thanks Nick! \r\nI think it really helps the reader interested in performing a similar task, from data preprocessing to choosing, training and evaluating a suitable model!\r\nI especially like the model checks (accuracy vs. confidence), and I also think that an extremely interesting continuation would be handling streaming data.\r\nIn my view it isn't too long either, and the visualizations are not only pretty but add a lot of information to the text!\r\n\r\nI'll split up my comments into 3 parts - content, formatting and typos etc.\r\n\r\n\r\n#### Content\r\n\r\n1.  I think I'd stress somewhere - either in the introduction or later, when reporting accuracy - that these are _really_ the raw data, i.e., not even noise filtered (read that on the website). I think that makes the results even more impressive.\r\n\r\n2. When restricting the set of activities used, you motivate this in a purely technical way. I find it interesting that we end up with activities that all are in fact activity changes (or transitions): x_to_y. So an alternative (or supplementary, rather, because the technical issue is of course really there) could be to say we're detecting transitions between activities... Just a thought :-)\r\n\r\n3. I was wondering what kind of accuracy you get using a bit less dropout... From the learning curves, performance on the validation set is better than on the training set very soon - so the training set is being underfitted, practically... I think I'd prefer reporting a learning curve where the performances are as close as possible, but if I have to  decide I'd like performance on the training set to be just a bit better, so no one says I'm underfitting the training set...\r\n\r\n4. For the abstract, I'd perhaps rephrase/reorder it a bit to make it more distant from the title\r\n\"In this post, we're training a convolutional neural network to classify changes in physical activity, as measured by accelerometer and gyroscope data from smartphones\" or something like that?\r\n\r\n\r\n#### Formatting\r\n\r\n1. I'd perhaps reformat some code blocks so you don't have to use the scroll bar to read the code (especially with the longer blocks)\r\n\r\n2. I have no idea but @jjallaire surely knows :-) Wonder if with the new technology, we can provide copy buttons to make copying out code easier?\r\n\r\n3. It looks like linked fixed-width text gets very large - like in  \" two layer_conv_1ds we \"\r\n\r\n4. I know I'm being totally pedantic so I'll say  just because I noticed ;-)): there are two different kinds of quotation marks in the model definition ;-) \r\n\r\n\r\n#### Typos etc\r\n\r\n1. \"turn our observation classes from integers to a one-hot, or dummy encoded, vectors\" -> one a too much\r\n\r\n2. \"using a confusion confusion matrix\"\r\n\r\n"}}
{"actor":{"display_login":"skeydan"}}