Neural Network Architecture and Optimization
An in-depth study is conducted on the internal structures of neural networks and their learning dynamics. Students examine feedforward architectures, weight initialization, and gradient-based optimization. Techniques such as learning rate scheduling and regularization methods help stabilize training. Challenges like vanishing and exploding gradients are discussed. This emphasizes designing efficient and scalable models. It enhances understanding of neural network behavior.
Architecture Components:
- Layer design and connectivity
- Optimization strategies
- Training stability techniques
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