Course 2 - Improving Deep Neural Networks

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Last Updated: Monday, 14 December 2020 00:40

Published: Thursday, 29 October 2020 18:23

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Course 2 - Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

This course bulids on deep NN. It has various techniques to optimize our NN to predict better. In absence of right parameters, your NN may not even work. It is a course that can be finished at a good speed. It has multiple python exercises, which should be completed. It has 3 sections:

1. Practical Aspects of Deep Learning: This talks about how to adjust parameters like initialization values, and how to choose initial values that will make our NN work. Can be watched in fast mode. However, the 3 exercises whould be finished. They don't take too much time.

2. Optimization algorithms: This goes over how to optimize the algo for finding thew lowest cost. It talsk about techniques of gradient descent (gd) as mini batch, gd with momentum, gd with RMs prop, gd with Adam and learning rate decay. There is a programming assignment to apply these various techniques on a NN, and observe the impact.

3. Hyperparameter tuning, Batch Normalization and Programming frameworks: This introduces google's framework called TensorFlow where we write a program to classify sign digits from 0 to 5.