As the course progresses, students will explore advanced topics, including complex neural network architectures such as CNNs, RNNs, and Transformers. It also dives into transfer learning, custom layers, loss functions, and model optimization techniques. Learners will practice building real-world projects, such as image classifiers, NLP-based sentiment analyzers, and GAN-powered applications.
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