Practice Activation functions: ReLU, Sigmoid, Tanh - 1.2 | Deep Learning Architectures | Artificial Intelligence Advance
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does ReLU stand for?

💡 Hint: Think about the shape of its graph.

Question 2

Easy

What range does the Sigmoid function output?

💡 Hint: Consider how it behaves at extreme inputs.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is the primary purpose of using activation functions in neural networks?

  • To introduce linearity
  • To introduce non-linearity
  • To output probabilities

💡 Hint: Consider what happens if there are no activation functions.

Question 2

The output range of the Tanh function is:

  • 0 to 1
  • -1 to 1
  • 0 to infinity

💡 Hint: Visualize their range on the graph.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design a neural network architecture for a binary classification problem including how you would incorporate activation functions.

💡 Hint: Focus on the advantages of each activation function in context.

Question 2

Investigate a scenario using Tanh in a specific type of network architecture and justify its advantages over using ReLU.

💡 Hint: Reflect on handling sequential data and its requirements.

Challenge and get performance evaluation