Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN Learning Rate

The video tutorial discusses the concept of learning rate in deep neural networks, highlighting its role as a step size in the error surface. It explains the challenges in determining the optimal step size and the potential for overshooting. Various heuristics, algorithms, and schedulers are mentioned as methods to adapt the learning rate. The tutorial emphasizes the importance of not keeping the learning rate fixed and suggests scheduling strategies, such as decreasing the rate as epochs increase. It concludes by stressing the need for tuning the learning rate through validation, as there is no guaranteed best approach.

What is the primary role of the learning rate in training models?

• To decide the type of activation function used

• To set the initial weights of the model

• To control the size of steps taken on the error surface

• To determine the number of layers in a neural network

What is a potential risk when using a large learning rate?

• Increasing the number of epochs

• Overshooting the optimal value

• Reducing the model's complexity

• Underfitting the model

Why is it challenging to find the best learning rate?

• Because it depends on the number of layers in the network

• Because it requires complex mathematical calculations

• Because it changes with every dataset

• Because there is no definitive method to determine it

What is a common practice regarding learning rate during training?

• Decreasing it as epochs progress

• Increasing it as epochs progress

• Keeping it constant throughout

• Randomly changing it at each epoch

Why is it important to tune the learning rate through validation?

• To adapt the learning rate to the specific dataset

• To reduce the number of parameters in the model

• To guarantee the model reaches the global minimum

• To ensure the model trains faster