Practice Activation Functions (7.3.2) - Deep Learning and Neural Networks
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Activation Functions

Practice - Activation Functions

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is the primary purpose of activation functions?

💡 Hint: Consider what allows a neural network to learn complex patterns.

Question 2 Easy

Name one activation function that outputs values between -1 and 1.

💡 Hint: Think about functions that can represent negative values.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the main function of activation functions in neural networks?

To introduce linearity
To increase computational load
To introduce non-linearity

💡 Hint: Think about what allows neural networks to model complex patterns.

Question 2

True or False: The ReLU activation function can yield negative outputs.

True
False

💡 Hint: Focus on how the ReLU function behaves with negative inputs.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Given a deep neural network with multiple layers using Sigmoid activation, describe the potential effects of the vanishing gradient problem on the training process.

💡 Hint: Consider how the gradients are calculated through the layers during backpropagation.

Challenge 2 Hard

A neural network is tasked with predicting both positive and negative outcomes. Would you recommend using Sigmoid or Tanh activation functions for the output layer? Justify your answer.

💡 Hint: Think about the output requirements for the specific task.

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