Applying optimization algorithms to deep neural networks presents distinct challenges compared to simpler models. The highly non-convex nature of the loss functions, combined with the sheer scale and high dimensionality of modern networks, requires specific attention.

In this chapter, you will learn about:

• The specific characteristics of loss functions encountered in deep learning, including phenomena like saddle points and plateaus.
• How network architecture choices, such as depth and width, interact with the optimization process.
• The role of techniques like Batch Normalization and Layer Normalization in stabilizing and accelerating training.
• Methods for addressing gradient-related problems, specifically vanishing and exploding gradients, using techniques like gradient clipping.
• The importance of weight initialization strategies (e.g., Xavier, He initialization) for successful optimization.
• How common regularization methods implicitly affect the optimization objective.

Understanding these issues is key to effectively training complex deep learning models. We will examine practical strategies and heuristics used to navigate these difficulties.