Introduction to Model Optimization
Explore how to optimize deep learning models by adjusting hyperparameters like model complexity, dropout, and activation functions. Understand training parameters such as learning rate, epochs, and batch size. Learn the role of validation sets to improve model generalization and prevent overfitting, enhancing your Keras model performance.
Why is optimization hard?
The optimal value of one weight depends on the value of the other weights. Many weights are optimized at once. From the slope, we will understand which weights to increase or decrease. However, the updates may not improve the model meaningfully. This calls for changing the hyperparameters.
Hyperparameters: Optimization methods
There are various ways to optimize the hyperparameters using trial and error. However, there is not a consensus on what works best.
Hyperparameters related to a neural network structure
1. Model complexity
This defines hidden layers or nodes per layer. The increase in model complexity usually enhances accuracy.
📝 Learn more about model complexity.
2. Dropout ...