This chapter provides a collection of recipes on the many aspects involved in the learning process of a neural network. The overall objective of the recipes is to provide very neat and specific tricks to boost networks' performances.
- Visualizing training with TensorBoard and Keras
- Working with batches and mini-batches
- Using grid search for parameter tuning
- Learning rates and learning rate schedulers
- Comparing optimizers
- Determining the depth of the network
- Adding dropouts to prevent overfitting
- Making a model more robust with data augmentation
