Generative AI Certificate Q&A: Which layer of neural network will have probability score?

Question

Kira is building a neural network to identify customer returns using binary classifications of defective or unsatisfied. In which layer of this neural network will Kira have a probability score?

A. the hidden layers
B. the input layer
C. the output layer

Answer

C. the output layer

Explanation

The answer to the question is C. the output layer. Here is an explanation:

A neural network is a machine learning model that consists of layers of interconnected units called neurons. Each layer receives input from the previous layer, performs some computation, and passes the output to the next layer. The first layer is called the input layer, the last layer is called the output layer, and the layers in between are called hidden layers.

The output layer of a neural network is responsible for producing the final prediction or decision based on the input data. Depending on the type of problem, the output layer can have different activation functions that determine how the output is calculated. For example, for a regression problem, where the output is a continuous value, the output layer can have a linear activation function that simply returns the weighted sum of the inputs. For a classification problem, where the output is a discrete label or category, the output layer can have a softmax activation function that returns a vector of probabilities for each possible class.

In this case, Kira is building a neural network to identify customer returns using binary classifications of defective or unsatisfied. This means that the output layer will have two neurons, one for each class, and a softmax activation function that will return a probability score for each class. The probability score indicates how likely the input data belongs to that class, based on the learned features and weights of the neural network. The class with the highest probability score will be the predicted label for the input data.

For example, if the input data is an image of a product that was returned by a customer, the output layer might return a probability score of 0.8 for defective and 0.2 for unsatisfied. This means that the neural network predicts that there is an 80% chance that the product was returned because it was defective and a 20% chance that it was returned because the customer was unsatisfied.

Reference

• machine learning – Neural networks output probability estimates? – Cross Validated (stackexchange.com)
• Probabilistic neural network – Wikipedia
• A guide to generating probability distributions with neural networks | by Sam Blake | HAL24K TechBlog | Medium
• Configuring a Neural Network Output Layer | Enthought, Inc.
• How to convert the output of an artificial neural network into probabilities? – Stack Overflow
• Binary Classification with Neural Networks – Atmosera
• 3.3. Metrics and scoring: quantifying the quality of predictions — scikit-learn 1.3.0 documentation
• Binary Classification Tutorial with the Keras Deep Learning Library – MachineLearningMastery.com
• A Gentle Introduction to Probability Scoring Methods in Python – MachineLearningMastery.com

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