Learning Rate Decay (C2W2L09)  Summary and Q&A
TL;DR
Learning rate decay helps to speed up learning algorithms by gradually reducing the learning rate over time.
Key Insights
 ☠️ Learning rate decay helps minimize noise and improve convergence in minibatch gradient descent.
 😚 Gradually reducing the learning rate allows for smaller steps closer to the minimum and prevents wandering behavior.
 ☠️ Different formulas can be used for learning rate decay, including exponential decay and discrete staircase decay.
Transcript
one of the things that might help speed up your learning algorithm is to slowly reduce your learning rate over time we call this learning rate decay let's see how you can implement this let's start  an example of why you might want to implement learning rate decay suppose you're implementing mini batch gradient descent with a reasonably small mini... Read More
Questions & Answers
Q: Why might you want to implement learning rate decay?
Learning rate decay is beneficial when using minibatch gradient descent with small minibatches, as it reduces noise and prevents the algorithm from wandering around without converging.
Q: How can learning rate decay be implemented?
Learning rate decay can be implemented by adjusting the learning rate using a formula that takes into account the number of epochs and a decay rate. The learning rate gradually decreases over time, allowing for slower and smaller steps.
Q: What are some other formulas for learning rate decay?
Besides the formula mentioned, other options for learning rate decay include exponential decay, decay based on epoch number and minibatch number, and discrete staircase decay.
Q: Is manually controlling the learning rate a viable option?
Manually controlling the learning rate can be effective when training only a small number of models. However, it is not practical for largescale training and is often replaced by automated techniques.
Summary & Key Takeaways

Learning rate decay is useful in minimizing noise and ensuring convergence in minibatch gradient descent algorithms.

By gradually reducing the learning rate, the algorithm takes smaller steps towards the minimum, leading to better convergence.

Implementing learning rate decay involves adjusting the learning rate based on the number of epochs and a decay rate.