Deep Learning with Python: Optimizing Deep Learning Models

Introduction

• Optimizing deep learning models
• What you should know
• Using the exercise files

1. Optimizing Deep Learning Models

• The importance of optimizing deep learning models

2. Regularization Techniques

• The bias-variance trade-off
• Lasso and ridge regularization
• Applying L1 regularization to a deep learning model
• Applying L2 regularization to a deep learning model
• Elastic Net regularization
• Dropout regularization
• Applying dropout regularization to a deep learning model

3. Loss Functions and Optimization Algorithms

• Common loss functions in deep learning
• Batch gradient descent
• Stochastic gradient descent (SGD)
• Mini-batch gradient descent
• Adaptive Gradient Algorithm (AdaGrad)
• Root Mean Square Propagation (RMSProp)
• Adaptive Delta (AdaDelta)
• Adaptive Moment Estimation (Adam)

4. Hyperparameter Tuning Techniques

• Parameters versus hyperparameters
• Key hyperparameters in deep learning
• Methods for hyperparameter tuning
• Defining a tunable deep learning model in Keras
• Using KerasTuner for hyperparameter tuning

5. Advanced Training Techniques

• Batch normalization
• Applying batch normalization to a deep learning model
• Gradient clipping
• Applying gradient clipping to a deep learning model
• Early stopping and checkpointing
• Learning rate scheduling
• Training a deep learning model using callbacks

Conclusion

• Continuing to optimize deep learning models