learn-co-students/dsc-tuning-neural-networks-recap-online-ds-ft-071519

Tuning Neural Networks - Recap

Key Takeaways

The key takeaways from this section include:

• Validation and test sets are used when iteratively building deep neural networks
• Like traditional machine learning models, we need to watch out for the bias variance trade-off when building deep learning models
• Several regularization techniques can help us limit overfitting: L1 Regularization, L2 Regularization, Dropout Regularization, etc ...
• Training of deep neural networks can be sped up by using normalized inputs
• Normalized inputs can also help mitigate a common issue of vanishing or exploding gradients
• Examples of alternatives for gradient descent are: RMSprop, Adam, Gradient Descent with Momentum, etc.
• Hyperparameter tuning is of crucial importance when working with deep learning models, as setting the parameters right can lead to great improvements in model performance