Deploying the sophisticated CNNs we've studied often encounters practical limitations like memory constraints, computational budgets, and latency requirements, especially on edge devices or mobile platforms. This chapter focuses on techniques to create more efficient deep learning models without significantly sacrificing performance.

You will learn methods to reduce model size and computational cost. We will cover:

• Network Pruning: Removing less important weights or structures from a network.
• Knowledge Distillation: Training a smaller 'student' model to mimic a larger 'teacher' model.
• Quantization: Reducing the numerical precision of model weights and activations, for example, from 32-bit floating point ($FP32$) to 8-bit integers ($INT8$).
• Efficient Architectures: Exploring model designs inherently optimized for speed and size, such as MobileNets.
• Neural Architecture Search (NAS): An overview of automated methods for finding efficient network structures.

The goal is to equip you with strategies for optimizing deep learning models for deployment in resource-constrained environments. We will also include a practical session applying some of these techniques.