Deep Learning CNN Convolutional Neural Networks with Python - Batch MiniBatch Stochastic Gradient Descent

The video tutorial discusses the significance of the learning rate in neural networks, highlighting its role in reaching the global minimum efficiently. It explores the challenges in selecting the optimal learning rate and compares different gradient descent methods, including batch, stochastic, and mini-batch gradient descent. The tutorial emphasizes the advantages of mini-batch gradient descent in terms of computational efficiency and smoother convergence.

What is the primary role of the learning rate in neural networks?

• To determine the number of layers in the network

• To decide the activation function used

• To set the initial weights of the network

• To control the step size in gradient descent

Why is it challenging to find the best learning rate for a dataset?

• Because it changes with every epoch

• Because there is no theoretical way to determine it

• Because it is influenced by the type of activation function

• Because it depends on the number of neurons

Which of the following is a common heuristic for starting learning rate?

• 0.01

• 0.1

• 0.001

• 0.0001

What is a key disadvantage of using batch gradient descent?

• It updates parameters too frequently

• It requires a lot of computational resources

• It converges too quickly

• It is not suitable for small datasets

How does stochastic gradient descent differ from batch gradient descent?

• It is only used for small datasets

• It requires more computational resources

• It updates parameters after each example

• It updates parameters after each epoch

What is the main advantage of mini-batch gradient descent over other methods?

• It is the fastest method available

• It combines the benefits of both batch and stochastic methods

• It requires no computational resources

• It does not require a learning rate

Why is mini-batch gradient descent preferred in practice?

• It requires fewer epochs

• It provides a smoother convergence

• It does not need a learning rate

• It is easier to implement