Welcome to the Improving Deep Neural Networks course in the Deep Learning Specialization! This module focuses on advanced techniques to enhance the performance of deep learning models.

Key Topics Covered

  • Hyperparameter Tuning 🔧
    Learn how to optimize learning rates, batch sizes, and network architectures.

    Hyperparameter Tuning

  • Regularization Techniques 🛡️
    Explore methods like L2 regularization, dropout, and data augmentation to prevent overfitting.

    Regularization Techniques
    [Read more about regularization](/Documentation/en/Courses/DeepLearningSpecialization/Regularization)

  • Optimization Algorithms 🚀
    Dive into advanced optimizers such as Adam, RMSProp, and learning rate scheduling.

    Optimization Algorithms

Practical Tips

  • Use cross-validation to fine-tune hyperparameters.
  • Apply early stopping to avoid overfitting during training.
  • Experiment with batch normalization for faster convergence.

For hands-on practice, check out the Deep Learning Specialization lab to implement these techniques! 📚

Deep Learning Model