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Questions On Deep Learning Technique Essay

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1.4.3 Deep Learning Technique
Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task [12]. Deep learning is another Machine Learning (ML) algorithm. Deep learning is essentially a set of techniques that help you to parameterize deep neural network structures, neural networks with many, many layers and parameters. Deep Learning breaks down tasks in ways that makes all kinds of machine assists seem possible, even likely. The confusion matrix, in Figure 8 shows that the accuracy of this model is (90.80) with weighted average precision (91.37) greater than recall (91.11) and F1-score (91.24). From the above results, it appears that Deep Learning classifier achieve higher accuracy, precision, recall, and F1-score. Figure 11: Clustering accuracy using Deep Learning Technique
1.5 Results Comparison
Table 2: Performance Measures Comparison
Model Decision Trees Naïve Bays Deep Learning
Domain precision recall precision recall precision recall food 100.00 25.93 58.06 66.67 46.55 100.00 communication 63.77 95.65 88.89 86.96 100.00 100.00 education 83.54 88.26 88.65 88.26 90.22 88.26 medical 61.67 62.71

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