Reinforcement Learning and Deep RL Python Theory and Projects - DNN Gradient Descent Stochastic Batch Minibatch

The video tutorial discusses different gradient descent methods: stochastic, mini batch, and batch gradient descent. It explains the role of the bias term in neural networks, which allows hyperplanes to be positioned arbitrarily in space, enhancing representational power. The tutorial compares the computational resources and convergence rates of each gradient descent method, highlighting mini batch as a practical compromise. The video concludes with a preview of an animation and coding demonstration in the next video.

What is the primary purpose of the bias term in a neural network?

• To increase the learning rate

• To allow hyperplanes to leave the origin

• To reduce computational resources

• To decrease the number of epochs

Which gradient descent method updates the model parameters after each training example?

• None of the above

• Batch Gradient Descent

• Stochastic Gradient Descent

• Mini-Batch Gradient Descent

In batch gradient descent, when are the model parameters updated?

• After every two epochs

• After all training examples

• After a subset of training examples

• After each training example

What is a key advantage of mini-batch gradient descent?

• It requires no computational resources

• It combines benefits of both batch and stochastic methods

• It always converges faster than other methods

• It uses the entire dataset for each update

Why might batch gradient descent require more computational resources?

• It updates parameters after each example

• It requires more epochs to converge

• It processes the entire dataset at once

• It uses a smaller learning rate

Which method is likely to converge faster with fewer iterations?

• Stochastic Gradient Descent

• Batch Gradient Descent

• None of the above

• Mini-Batch Gradient Descent

What is an epoch in the context of gradient descent?

• A subset of training examples

• A single update of model parameters

• A measure of computational resources

• A complete pass through the entire training dataset