The document discusses overfitting and underfitting in machine learning, including methods to diagnose these issues. Solutions presented for overfitting include increasing training examples and employing regularization techniques such as L1, L2, and dropout. It also provides resources for further reading on these topics.

1. Overfitting , Underfitting,
and Regularization
Aly O. Abdelkareem

6. How to find
overfitting or
underfitting
?

7. Solutions for
overfitting?
1. Increase training
examples
2. Regularization
1. L2
2. L1
3. Dropout

9. Regularization

10. Dropout

11. Resources
• http://www.chioka.in/differences-between-l1-and-l2-as-loss-
function-and-regularization/
• https://medium.com/@amarbudhiraja/https-medium-com-
amarbudhiraja-learning-less-to-learn-better-dropout-in-deep-
machine-learning-74334da4bfc5