8.4.1. Algorithmic Optimization

Try these first

1. 1.

   Define model pruning.

   Hint

   Think about what happens when you reduce the complexity of a network.

2. 2.

   What is quantization?

   Hint

   Consider the difference between 32-bit and 8-bit data types.

3. 3.

   What is the main goal of algorithmic optimization?

   • Decrease accuracy
   • Reduce computational requirements
   • Increase memory size

   Hint

   Think about efficiency in computations.

4. 4.

   True or False: Model pruning increases the size of a neural network.

   • True
   • False

   Hint

   Consider what pruning means.

5. 5.

   Given a neural network with 1M weights, if applying model pruning reduces weights to 200K while maintaining 90% accuracy, discuss possible implications for deployment in edge devices.

   Hint

   Consider factors like processing time and memory in edge applications.

6. 6.

   Analyze the trade-offs involved with quantization in a deep learning model that previously used 32-bit floats. What are the possible impacts on performance and accuracy?

   Hint

   Reflect on the effects of reduced precision on output.