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This course covers deep learning fundamentals, from core machine learning concepts (e.g.: loss functions, bias-variance, and optimization) to advanced models like Multilayer Perceptrons, Convolutional Neural Networks, Recurrent Neural Networks, and Transformers. Students will gain hands-on experience by applying these techniques to real-world datasets using various deep learning frameworks.