Deep Learning - Deep Neural Network for Beginners Using Python - Implementing Logistic Regression

The video tutorial covers the implementation of a logistic regression algorithm. It begins with an introduction to various terminologies used in machine learning, such as features, targets, epochs, and learning rate. The instructor then defines global variables for epochs and learning rate, followed by a detailed explanation of the training code implementation. The tutorial also covers output calculation, error handling, and updating weights and bias. Finally, it discusses model evaluation, accuracy calculation, and visualization using a display function. The tutorial aims to familiarize viewers with the complete process of implementing logistic regression.

What is another term often used for 'features' in different contexts?

• Labels

• Y hat

• X

• Epochs

What is the purpose of setting a random seed in the context of training a model?

• To decrease the number of epochs

• To ensure reproducibility

• To randomize the output

• To increase the learning rate

What is the initial value set for the bias in the training loop?

• 0.5

• Random value

• 0

• 1

Which function is used to calculate the error in the training loop?

• Mean function

• Update weights function

• Error formula

• Output function

What is the role of the 'update weights' function in the training process?

• To adjust weights and bias based on error

• To calculate the output

• To set the learning rate

• To initialize the weights

What is calculated using the 'Numpy mean' function in the training loop?

• Total number of features

• Mean of the weights

• Mean of the loss

• Mean of the bias

What is the significance of the 'output formula' in the training loop?

• It sets the learning rate

• It calculates the model's output

• It initializes the weights

• It determines the number of epochs

What is the purpose of appending the mean loss to the 'errors' list?

• To calculate the bias

• To adjust the learning rate

• To visualize the training progress

• To initialize the weights

How often is the training progress printed during the epochs?

• Every epoch

• Every 5% of epochs

• Every 50% of epochs

• Every 10% of epochs

What is planned to be implemented in the upcoming lecture?

• A new learning rate

• A solution boundary

• A different error formula

• A new feature set