Deep Learning - Deep Neural Network for Beginners Using Python - Visualization and Results

The video tutorial covers the process of plotting solution boundaries and data points using visualization techniques. It discusses error plotting, debugging, and error handling in the code. The training process is explained with a focus on results analysis, including accuracy and loss metrics. The session concludes with a comparison between logistic regression and perceptron algorithms, highlighting their similarities and differences.

What is the purpose of using different colors when plotting solution boundaries?

• To make the plot more visually appealing

• To highlight errors in the code

• To differentiate between the solution boundary and original boundaries

• To indicate the speed of convergence

What is plotted on the X-axis of the error chart?

• Training loss

• Accuracy

• Number of epochs

• Learning rate

Why might the training code not produce any output initially?

• The data points are not plotted

• The learning rate is too high

• The train function is not called

• The number of epochs is too low

What is a common error when calculating loss in the training code?

• Incorrect number of epochs

• Not initializing weights

• Using the wrong learning rate

• Forgetting to pass the targets and output

How does increasing the number of epochs affect the training process?

• It has no effect

• It decreases the error and increases accuracy

• It increases the error

• It decreases the accuracy

What is the main advantage of using gradient descent in training?

• It allows the solution boundary to evolve gradually

• It increases the number of epochs

• It simplifies the code

• It decreases the learning rate

What is a key difference between logistic regression and perceptron algorithms?

• Logistic regression can handle non-linear boundaries

• Perceptron does not use a loss function

• Perceptron is used for regression tasks

• Logistic regression uses a linear activation function