This course teaches you how to evaluate and design custom neural network architectures for real machine-learning tasks. You start by learning how to compare common model families—such as CNNs, RNNs, and Transformers—and match them to task needs, data patterns, and compute limits. You then learn how to construct custom architectures using layers, activations, and regularization techniques that improve generalization and training stability. Through videos, readings, hands-on practice, and guided coach support, you build models in PyTorch and test how design choices affect performance. By the end of the course, you can confidently select topologies, justify architectural decisions, and design models ready for real-world deployment.