What Are Some Good Tips to Write out a Detailed PDF Or Word Document?

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What are some good tips to write out a detailed PDF or Word document in a Data Science project?

Choosing the problem This is, by far, the most important step. There are several factors that you need to consider here, other than whether you find the problem interesting or not. Do you have data or can you create / collect data in a reasonable amount of time? Estimate the compute resources (GPUs / compute time) etc. required — for instance, if you work with a massive dataset like ImageNet, it might take you days to train a NN for it, so the no. of experiments you would be able to run would be limited. Ideally, there should be some work similar to what you’re planning to do, but not a whole lot. That way, you have some ideas of where to start, and there would still be some relatively simple ideas that have not been explored so far. There should be someone local (professor, senior grad student, post-doc) who has some experience with the problem / techniques you are planning to use. This allows you to bounce your ideas off them, particularly when you get stuck. Working on the problem. Start simple. That’s true for pretty much any problem problem in AI — because most problems involve a lot of engineering, it is easy to go down the rabbit hole trying to make a really fancy model, which never gets implemented because of various complexities. So, for instance, if you are building an extractive summarization system (which takes a document and generates a summary by selecting important sentences from it), a simple model could involve building a bag-of-words representation of each sentence and of the entire document, and using cosine similarity between the two to score the sentence w.r.t. the document. Then, pick the top K sentences returned by this scoring function. Once you have a model that works, think about its weaknesses / analyse the cases where it fails. Then, improve your model to address these. For instance, in the above model, you may have realized that the values in the bag-of-words representation of the document would be much larger than those of the sentences; so maybe, normalize the vectors to have unit norm. Another direction to improve the model is, of course, to use richer representations of the sentence and the document. You may also notice that your data needs some processing. For instance, if you are working in NLP, you may want to throw away sentences that are really long / really short to reduce the noise in the data. You may need to handle unbalanced classes. Overall, you just have to repeat the process above — analyze the current model & improve! Reporting the results Here are several points to follow when reporting your findings. If you are comparing your system with a baseline, and your system involves multiple changes / additions to the baseline, report how adding each change incrementally affects the results, not just the total improvement. This helps your audience know which changes were the most useful. Use statistical tests, particularly when the improvement is small. For instance, if the accuracy of the baseline model is 85%, and the accuracy of your approach is 86%, it is not clear just from those two numbers whether your model was actually better, or you got that improvement by chance. Do not gloss over the weaknesses of your approach. If you discover that your approach is worse than some other method, don’t omit that result — mention it, and try to reason about why that’s true. Your reasoning should be written in a sound way — if you ran additional experiments to validate your hypotheses, talk about it; if you are just speculating, say so.

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Write on PDF: All You Need to Know

In general, if you can, try to avoid reporting any experimental results that were not published before. That way, you avoid the appearance of bias and, thus, don’t give the impression that you know what you're talking about. Also, whenever possible, includes a code snippet that demonstrates the effect of a change. The code snippet should show the effect of adding a feature, not its absence. In other words, if your code snippet demonstrates a statistical improvement that you did not actually measure, you can give an example of a result in which your code improved results, not just the general result. Also, if your results rely on a certain data structure (or API), do not just report a general “improvement”, but a specific example that illustrates the benefit of the improvement. This way, your viewers will have a clearer idea about what you’re talking about. Finally, you should never.