Deep Learning - Crash Course 2023 - Class Sigmoid

The video tutorial covers the implementation of a sigmoid neuron class, including methods for initialization, prediction, loss calculation, and accuracy assessment. The tutorial demonstrates how to calculate the weighted sum, predict outputs, compute mean squared error loss, and visualize loss reduction. It also explains binary classification by mapping outputs to binary values and calculating accuracy using the SKLN library. The tutorial concludes with running the model and observing improvements in accuracy.

What is the primary purpose of the 'init' method in the Sigmoid Neuron class?

• To predict the output

• To compute the gradients

• To calculate the loss

• To initialize weights and biases

Which method is used to calculate the predicted output in the Sigmoid Neuron class?

• sigmoid

• fit

• predict

• loss

What type of loss is used in the Sigmoid Neuron class?

• Hinge loss

• Mean squared error loss

• Cross-entropy loss

• Logarithmic loss

How is the loss visualized during the learning process?

• By printing the loss values

• By saving the loss values to a file

• By plotting the loss values

• By logging the loss values

What is the purpose of using a threshold in binary classification?

• To increase the learning rate

• To map outputs to binary values

• To decrease the number of epochs

• To improve the model's accuracy

Which library is used to calculate the accuracy score in the Sigmoid Neuron class?

• NumPy

• TensorFlow

• Scikit-learn

• Pandas

What can be adjusted to improve the accuracy of the Sigmoid Neuron model?

• Activation function

• Number of layers

• Learning rate and epochs

• Batch size