100 Days of Machine Learning Code Day 11

Day 11 : Finishing off with the  Week 1 of  "Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization" which included not 1 but 3 assignment notebooks on the topics of Initialization,Regularization  and Gradient checking

1. Topic  : Initalization , My notebook 

• Understand that different initialization methods and their impact on your model performance
• Implement zero initialization and and see it fails to "break symmetry",
• Recognize that random initialization "breaks symmetry" and yields more efficient models,
• use both random initialization and scaling to get even better training performance on model.

2.  Topic : Regularization , My notebook 

• different regularization methods that could help  model.
• Implement dropout and see it work on data.
• Recognize that a model without regularization gives  a better accuracy on the training set but nor necessarily on the test set.
• Understand that you could use both dropout and regularization on your model.

3. Topic : Gradient Checking , My Notebook

• Implement gradient checking from scratch.
• how to use the difference formula to check your backpropagation implementation.
• Recognizing that  backpropagation algorithm should give you similar results as the ones you got by computing the difference formula.
• how to identify which parameter's gradient was computed incorrectly.