5Advanced Modeling and Deep Learning

• 5.1Neural Network Fundamentals

  The foundation of deep learning—understand layers and neurons, activation functions (ReLU/sigmoid/softmax), backpropagation, learning rate/epochs/batch, and the vanishing gradient.

• 5.2Deep Learning Architectures

  Architectures by data—understand CNN (images), RNN/LSTM (sequences), Transformer (language), and choosing by use case. Pick architecture by the nature of the data.

• 5.3Transfer Learning and Tuning Deep Nets

  Smart with little data—understand transfer learning/fine-tuning, SageMaker JumpStart, dropout/batch normalization, data augmentation, and early stopping.