Neural Networks: Regularization & Optimization

This lecture covers the fundamentals of neural networks, focusing on regularization techniques such as L1, L2, and dropout to prevent overfitting. It also delves into batch normalization, residual networks, and transfer learning strategies. Practical tips on implementing neural networks, including hyperparameter tuning and monitoring the training process, are discussed.