Improving Deep Neural Networks: Hyperparameter Tuning, Regularization & Optimization 🧠🔧📊

Key Concepts in Deep Learning Optimization

1. Hyperparameter Tuning

   • Learning rate (e.g., Stochastic_Gradient_Descent)
   • Batch size (e.g., Mini_Batch_Training)
   • Number of layers/units (e.g., Deep_Learning_Architecture)
   

2. Regularization Techniques

   • L1/L2 regularization (e.g., Weight_Constraint)
   • Dropout (e.g., Neuron_Dropout)
   • Early stopping (e.g., Training_Validation)
   

3. Advanced Optimization Algorithms

   • Momentum (e.g., Gradient_Descent_Momentum)
   • Adam optimizer (e.g., Adam_Optimizer)
   • Learning rate scheduling (e.g., Cosine_Decay)
   

Practical Tips for Better Performance

• Use cross-validation to avoid overfitting
• Monitor training loss and validation accuracy
• Experiment with batch normalization for faster convergence

Recommended Reading

• Neural Networks Primer for foundational concepts
• Deep Learning Specialization on Coursera for advanced topics

Tools & Resources

• TensorBoard for visualization
• Keras Tuner for hyperparameter optimization