Deep Learning and Neural Networks Questions

Foundations and practical considerations of neural networks and deep learning. Includes network structure and components such as input hidden and output layers, activation functions, forward propagation and back propagation, weight initialization, loss functions, and optimization algorithms. Covers common architectures and when to use them, including convolutional neural networks for images, recurrent and sequence models for time series and text, and modern transformer architectures for language and other modalities. Discusses representation learning, data requirements for deep models, regularization techniques, transfer learning and fine tuning, interpretability limitations, and when deep learning is justified versus simpler models.