Practice Training Deep Networks - 8.3 | 8. Deep Learning and Neural Networks | Data Science Advance
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8.3 - Training Deep Networks

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is backpropagation?

💡 Hint: Think about how we correct mistakes after getting our predictions.

Question 2

Easy

What does gradient descent aim to minimize?

💡 Hint: Consider what error we want to reduce in our predictions.

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 does backpropagation help optimize in neural networks?

  • Weight loss
  • Gradient descent
  • Loss function

💡 Hint: Consider what we are trying to minimize by optimizing our predictions.

Question 2

True or False: Overfitting results in better model accuracy on unseen data.

  • True
  • False

💡 Hint: Think about what happens when a model memorizes the training data.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You have a neural network with 5 hidden layers. During training, it experiences vanishing gradients, which seem to halt learning. Describe methods you might implement to alleviate this issue.

💡 Hint: Consider how architectures help control gradient behavior.

Question 2

Design an experiment comparing the effectiveness of Batch Gradient Descent to Mini-batch Gradient Descent on a set of training data. Outline the criteria you would measure and hypothesize expected outcomes.

💡 Hint: Think about the practical implications of each method's data processing.

Challenge and get performance evaluation