| Catalog Content |
This course introduces the core concepts, architectures, and techniques of deep learning. Students learn about neural network structures, activation functions, backpropagation, and optimization methods. The course covers feedforward, convolutional, and recurrent networks, as well as regularization, dropout, and transfer learning. Practical exercises focus on training and evaluating models on large datasets, applying deep learning to image recognition, natural language processing, and time series analysis, and developing the skills to design, implement, and fine-tune deep models for real-world big data analytics tasks. |
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