|For how many years have you been experimenting with data?||
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Overall you show great understanding of the problem and the data itself. The structure of the article is well formed. However there are a few areas in which you can improve.
First in your evaluation don’t just add the list of pairs. It is very difficult to tell if which is the parent of which just by looking at the list. Especially if you’ve never heard of Danaher_Corporation and Pall_Corporation. Who’s the parent and who’s the subsidiary? Was your model correct in predicting this relationship? Add for instance your precision or recall values or add a confusion matrix? Also among these pairs which did the algorithm misclassify and in your opinion why?
Second and this one is really minor so don’t worry much about it. If you add an abstract don’t make it too detailed. Abstracts are for people who are not experts and are not familiar with NLP. It should be a simple layman’s description of what the problem is and how you propose to solve it. Leave the detailed description for the Introduction.
You’ve presented your solution and test results very well. However you shouldn’t just copy the case description as a business understanding. In this section you need to show that you understand what the problem is, who the users of your solution are, and what they expect to see from the system. Good presentation overall and a very interesting article to read. Well done Team Cherry!!
Good first attempt. I think you can improve on your Neural network architecture. Read more about convolutional neural networks they are a great help with image recognition tasks
(+) Well formed article, shows understanding of the problem and task at hand
(+) You’ve handled the insufficient data problem well and you’ve presented a good description of the model
(-) Providing raw results is not very helpful you should add some analysis as to why you think you got the results that you have.
(~) Also would be nice to know more about the actual performance of the models. The most accurate model is not always the best choice especially if it requires expensive hardware to run and you don’t have the budget for it.
You show good understanding of the data set and the challenges that it represents.
You’ve managed to find an interesting workaround some of the issues mainly orientation and lighting.
However there are some areas that you can improve in. First is the business understanding. You need to show that you have understood the problem domain and who the final users of the system will be. What their expectations are? In the model section you make a very good argument as to why you’ve decided to use GoogleLenet, however you could go into more details about the advantages and disadvantages of using this tech as opposed to other solutions. In the evaluation section you need to provide an analysis of the results showing that you understand the inner workings of your system, not just raw results.
Also you should never provide the results from the training and validation sets
as those are considered training data and the results on them are misleadingly high, due to the fact that the system has seen them before. You should always have a separate set of data for testing purposes.