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It just variable to hold the value of data part of priority queue node.

@@aksharacsit Why we need "element" variable even we did not used for operation?

@@learn_something_g in real world programming it would be some thin like employee , student or any other object. Those objects will be stored in datastructure based on priority by using priority queue. Here priority or next fields are used to build priority queue datastructure. But our ultimate goal is to handle data(objects like employee, students) to hold that objects we need element field. To make program simple we are not considering objects inplace of that we are using simple integer element. In our program we assigned element to the priority queue data field we are using that.

@@aksharacsit Could you please make a video for explaining my doubt? I didn't understand what you said.

@@learn_something_g I will try to make a video on that. But it may take time. In priority queue node 3 fields are there Data field ->it will hold the actual data(object information). This is the important one To assign value to priority queue node data field we used element nn->data= element; In the create node function Priority field ->while storing data into our data structure it decide order Next filed -,> to hold the next node value(here we are using linked list to implement priority queue.

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Weak entity starts at @ 4:15 minutes

In ER (Entity-Relationship) mappings, derived attributes are typically not included as separate attributes in the entity model. Derived attributes are calculated or derived from other attributes in the entity, and they do not represent stored data in the database. Instead of including derived attributes in the entity model, they often calculated on-the-fly when needed or stored in a separate table or view. Including derived attributes in the entity model can lead to redundancy and potential data inconsistency.

Yes derived attributes will not be stored in database, but derived attribute values will be calculated based on already existing data. So in this relational mapping we will ignore derived attributes.

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Yeah it’s really nice of you sir

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I will do. Thank you for watching

Here s1 and s2 are sample standard deviations. By using standard deviation formula for above data you can find

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Initially wf and bf values will be randomly assigned they will be adjusted(learned) during training process based on output. Wf and bf are weight matrices like normal weights in neutral networks. They will be configured during training.

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