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LO-1
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Upon successful completion of this course, students will gain the ability to model complex datasets by deeply understanding the fundamental working principles of artificial neural networks, perceptrons, and backpropagation algorithms; they will be able to successfully apply feedforward, convolutional (CNN), and recurrent (RNN) network architectures in classification, regression, and sequence prediction tasks; and they will achieve the competence to integrate model training on large datasets, hyperparameter optimization, and fine-tuning processes into real-world machine learning projects using modern deep learning libraries. |