CPSCI-307 Topics in Computer Science II
This course discusses deep learning, covering foundational machine learning principles (loss functions, bias-variance trade-off, and optimization) and advanced models (Multilayer Perceptron, Convolutional Neural Networks, Recurrent Neural Networks, Transformers). The course is project-based, emphasizing the application of these techniques to real-world datasets using the deep learning framework PyTorch.
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Credits
1