Practice - Training Deep Neural Networks
Practice Questions
Test your understanding with targeted questions
What is normalization in the context of training deep learning models?
💡 Hint: Think about why scaling might be needed in data processing.
What does data augmentation help us achieve?
💡 Hint: Consider how it can help reduce model overfitting.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What is the purpose of normalization?
💡 Hint: Remember how scaling impacts the training process.
True or False: Data augmentation is performed on the validation dataset.
💡 Hint: Think about the purpose of validation datasets.
2 more questions available
Challenge Problems
Push your limits with advanced challenges
Design a deep learning training pipeline that includes dataset preparation, training phases, and hyperparameter tuning. Describe each step.
💡 Hint: Think about how each component influences the overall training success.
Consider you have a dataset with imbalanced classes. Suggest a strategy for data augmentation to address this issue while training a neural network.
💡 Hint: Consider methods to increase the presence of underrepresented classes.
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Reference links
Supplementary resources to enhance your learning experience.
- Normalization in Deep Learning
- Data Augmentation Techniques
- Understanding Epochs and Batch Size
- Hyperparameter Tuning with Grid Search
- Random Search vs. Grid Search
- Bayesian Optimization Explained
- Deep Learning Hyperparameter Tuning
- Introduction to Data Augmentation in Deep Learning
- Monitoring Loss and Accuracy in Neural Networks
- Understanding Training Phases in Deep Learning