Key Techniques in Machine Learning Optimization and Regularization

This lecture delves into core aspects of optimization in machine learning, covering its significance in deep learning and backpropagation, the role of activation and loss functions, and the importance of optimization algorithms like gradient descent variants and Adam. Regularization techniques such as L1, L2, dropout, and batch normalization are examined for their potential to prevent overfitting. The session concludes by addressing challenges, effective practices, and future trends in...