This chapter focuses on how to evaluate a neural network model. Different than working with other kinds of models, when working with neural networks, we modify the network's hyper parameters to improve its performance. However, before altering any parameters, we need to measure how the model performs.
By the end of this chapter, you will be able to:
- Evaluate a model
- Explore the types of problems addressed by neural networks
- Explore loss functions, accuracy, and error rates
- Use TensorBoard
- Evaluate metrics and techniques
- Hyperparameter optimization
- Add layers and nodes
- Explore and add epochs
- Implement activation functions
- Use regularization strategies
