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Confusion Matrix with Real-Life Examples || Artificial Intelligence || ~...

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Learn about the Confusion Matrix with Real-Life Examples. A confusion matrix is a table that shows how well an AI model makes predictions. It compares the actual results with the predicted ones and tells which are right or wrong. It includes True Positive (TP), False Positive (FP), False Negative (FN), and True Negative (TN). Video Chapters: Confusion Matrix in Artificial Intelligence 00:00 Introduction 00:12 Confusion Matrix 03:48 Metrices Derived from Confusion Matrix 04:26 Confusion Matrix Example 1 05:44 Confusion Matrix Example 2 08:10 Confusion Matrix Real-Life Uses #artificialintelligence #machinelearning #confusionmatrix #algorithm #optimization #research #happylearning #algorithms #meta #optimizationtechniques #swarmintelligence #swarm #artificialintelligence #machinelearning
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Loop Optimization Techniques in Compiler Design

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LOOP Optimization

Rules Every Programmer must follow before the optimization of any source code. 
1.) Loop transformation technique. 
2.) Help to reduce optimize the execution time of a program.
3.) Basically, remove the iterations. 
4.) Loop Unrolling increase the program speed by eliminating loop control instructions.

Loop Optimization is a Machine Independent Technique. We can do Code optimization in the loops of the program. In loop optimization, code optimization is performed on inner loops.

Methods of Loop Optimization
1.) Code Motion
2.) Loop Invariant Method
3.) Loop Jamming
4.) Loop Unrolling
5.) Strength Reduction
Code Motion: Technique that moves the code outside the loop.
Loop Jamming: Combining two or more loops in a single loop.
Code Strength: Expressions that consumes CPU time and memory should replace with cheaper expressions. Replace Expensive Expressions (i.e., *) with cheaper ones (i.e., +)

Vidoe: https://youtu.be/1cSnmjRPRTA
Loop Optimization Technique in Compiler Design

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