Deep Learning CNN Convolutional Neural Networks with Python - Weight Initialization Solution

The video tutorial explains the basic concepts of axes, convex functions, and global minima. It describes how a convex function, like a parabola, has a single lowest point known as the global minima. This point is crucial for optimizing a model's weights, ensuring minimal loss and optimal performance.

What are the two axes mentioned in the context of a simple convex function?

• Y axis and Z axis

• Z axis and W axis

• X axis and Z axis

• Y axis and X axis

Which shape is used to represent a simple convex function?

• Square

• Triangle

• Circle

• Parabola

What is the term used for the single lowest point of a convex function?

• Global minima

• Local maxima

• Local minima

• Global maxima

Why is the global minima important for a model?

• It decreases the accuracy

• It minimizes the loss

• It increases the complexity

• It maximizes the loss

At the global minima, what aspect of the model is optimized?

• The model's color

• The model's speed

• The model's size

• The model's weights