Topic 04: Optimization - Part II

This chapter explores advanced topics in neural network optimization, covering key challenges in training stability, initialization techniques, momentum and adaptive learning rates, and activation functions. Addressing issues like ill-conditioning, local minima, and exploding gradients, the chapter discusses methods to improve model convergence and performance, including practical weight initializations, learning rate schedules, and specialized activations for hidden and output layers to overcome common obstacles in deep learning.