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In this post, I will share six concepts of the book in my own language out of my understanding.
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NG emphasizes throughout the book that it is crucial to iterate quickly since machine learning is an iterative process. Instead of thinking about how to build the perfect ML system for your problem, you should build a simple prototype as fast as you can. This is especially true if you are not an expert in the domain of the problem since it is hard to correctly guess the most promising direction.
You should build a first prototype in just a few days and then clues will pop up that show you the most promising direction to improve the performance of the prototype.
Yearning | Definition of Yearning by Merriam-Webster
In the next iteration, you will improve the system based on one of these clues and build the next version of it. You will do this again and again. He goes on explaining that the faster you can iterate, the more progress you will make. Other concepts of the book, build upon this principle. Note that this is meant for people who just want to build an AI-based application and not do research in the field. This concept builds upon the previous one and the explanation about why you should choose a single-number evaluation metrics is very simple: It enables you to quickly evaluate your algorithm and therefore you are able to iterate faster.
Using multiple evaluation metrics simply makes it harder to compare algorithms. Imagine you have two algorithms. The problem is, that you lose a lot of time for this task at every iteration and that it adds up over the long run. You will try a lot of ideas about architecture, parameters, features, etc.
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If you are using a single-number evaluation metric such as precision or the f1-score , it enables you to sort all your models according to their performance, and quickly decide which one is working best. Another way of improving the evaluation process would be to combine several metrics into a single one, for example, by averaging multiple error metrics.
Nevertheless, there will be ML problems that need to satisfy more than one metric, like for example: taking running time into consideration.
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In short, a single-number evaluation metrics enables you to quickly evaluate algorithms, and therefore to iterate faster. Error analysis is the process of looking at examples where your algorithms output was incorrect. For example, imagine that your cat detector mistakes birds for cats and you already have several ideas on how to solve that issue.
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This enables you to decide which idea is the best to spend your resources on. Furthermore, it enables you to quickly judge several ideas for improvement in parallel. You just need to create a spreadsheet and fill it out while examining, for example, misclassified dev set images. On the spreadsheet, you create a row for every misclassified image and columns for every idea that you have for improvement.
Then you go through every misclassified image and mark with which idea the image would have been classified correctly. Then you know that working on idea-1 is the most promising improvement that your team should work on. Also, once you start looking through these examples, you will probably find new ideas on how to improve your algorithm. The optimal error rate is helpful to guide your next steps. In statistics, it is also often called the Bayes error rate. It also helps you to detect if you are algorithm is suffering from high bias or variance, which helps you to define the next steps to improve your algorithm.
But how do we know what the optimal error rate is? For tasks that humans are good at, you can compare your system's performance to those of humans, which gives you an estimate of the optimal error rate. In other cases, it is often hard to define an optimal rate, which is the reasons why you should work on problems that humans can do well, which we will discuss at the next concept. Throughout the book, he explains several times why it is recommended to work on machine learning problems that humans can do well themselves.
This has several reasons. First, it is easier to get or to create a labeled dataset, because it is straightforward for people to provide high accuracy labels for your learning algorithm if they can solve the problem by themselves. Second, you can use human performance as the optimal error rate that you want to reach with your algorithm.
It also helps you to detect if your algorithm is suffering from high bias or variance. Third, it enables you to do error analysis based on your human intuition. If you are building, for example, a speech recognition system and your model misclassifies its input, you can try to understand what information a human would be using to get the correct transcription, and use this to modify the learning algorithm accordingly.
One-star words are frequent, two-star words are more frequent, and three-star words are the most frequent. The thesaurus of synonyms and related words is fully integrated into the dictionary. Click on the thesaurus category heading under the button in an entry to see the synonyms and related words for that meaning. This is the British English definition of yearning.
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View American English definition of yearning. Change your default dictionary to American English. View the pronunciation for yearning. Definition and synonyms of yearning from the online English dictionary from Macmillan Education.
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