Python for Deep Learning - Build Neural Networks in Python - Adding the Output Layer

The video tutorial explains the use of a single neuron in the output layer for binary classification tasks, such as predicting whether a customer will leave or stay. It details the configuration of the output layer using a dense unit with a sigmoid activation function, which provides the probability of an outcome. The tutorial concludes with a transition to the next lecture.

Why is only one neuron sufficient in the output layer for this dataset?

• Because it improves computational efficiency

• Because it reduces overfitting

• Because the dependent variable is binary

• Because the dataset is very large

What is the purpose of setting 'units' to one in the output layer?

• To increase the number of predictions

• To enhance the model's complexity

• To match the number of input features

• To ensure a single output for binary classification

Which activation function is used in the output layer for binary classification?

• ReLU

• Tanh

• Sigmoid

• Softmax

What does the sigmoid activation function provide in the context of this model?

• The average of the input data

• The exact value of the output

• The probability of a binary outcome

• The sum of all input features

In the context of this model, what does a sigmoid output close to 1 indicate?

• The customer will definitely stay

• The customer might stay

• The customer will definitely leave

• The customer might leave