https://www.freecodecamp.org/news/big-o-notation-why-it-matters-and-why-it-doesnt-1674cfa8a23c/
"Elsevier อนุญาตให้ใช้ AI ช่วยเขียน manuscript ได้แล้ว แต่ใช้สำหรับการปรับปรุงภาษาและสไตล์การเขียนเท่านั้น ห้ามใช้สร้างข้อมูล, วิเคราะห์ข้อมูล หรือแปลผล สรุปผล และ authors ต้องแจ้งเมื่อ submit manuscript เสมอ ห้ามใส่ AI เป็น co-author" --
A CMS system that can automatically check the answers to programming questions by using provided test cases.
https://www.domjudge.org/
คือการ normalize output ของแต่ละ node เป็นค่า standardized values และเนื่องจากค่า standardized value = (x-mean)/SD ค่า mean & SD จะคำนวนมาจาก node ที่อยู่ใน layer เดียวกัน
Because the normalizatin occurs on a per batch basis, hence the name batch normalization. The batch size is a number of samples processed before the model is updated. The number of epochs is the number of complete passes through the training dataset. The size of a batch must be more than or equal to one and less than or equal to the number of samples in the training dataset.
ประโยชน์คือทำให้การ train เร็วขึ้น เพราะค่า output ของแต่ละ node ไม่ต่างกันมากไป
https://www.analyticsvidhya.com/blog/2021/06/confusion-matrix-for-multi-class-classification/
You have different options when calculating quality metrics in multi-class classification.
1.Calculating precision and recall by class is useful when you want to evaluate the performance of a classifier for a specific class of interest or when dealing with imbalanced classes, but it can result in a large number of performance metrics.
When you have a large number of classes or want a more concise summary of overall performance, using macro or micro averages can be a better option.
2.Macro-averaging shows average performance across classes, treating each class as equally important.
3.Micro-averaging gives equal weight to every instance and shows average performance across all predictions. In the case of multi-class classification, micro-averaged precision, recall, and accuracy are the same.
4.You might also consider using weighted averaging based on the proportion each class takes in the dataset. This approach is useful if you have an imbalanced dataset but want to assign larger importance to classes with more examples.
Recap
https://www.evidentlyai.com/classification-metrics/multi-class-metrics
Analysis of large/complex information system should start with business process modeling by using activity diagram and place ovals (use cases) over a set of activities to derive use case diagram. Then each use case has its activity diagram drawed.