Month 20: Hyperparameter Tuning and Optimization

Week 1: Introduction to Hyperparameter Tuning and Optimization

• Overview of Hyperparameter Tuning and Optimization
• Types of Hyperparameters
• Grid Search and Random Search

Week 2: Gradient-Based Optimization Techniques

• Gradient Descent
• Stochastic Gradient Descent (SGD)
• Mini-Batch Gradient Descent
• Momentum-based Optimization Techniques (Nesterov, Adagrad, Adadelta, Adam)

Week 3: Bayesian Optimization Techniques

• Introduction to Bayesian Optimization
• Gaussian Processes
• Acquisition Functions
• Bayesian Optimization Libraries (e.g., Hyperopt, Optuna)

Week 4: Automated Machine Learning

• Introduction to Automated Machine Learning
• AutoML Techniques (e.g., TPOT, H2O.ai, Auto-Keras)
• Model Selection and Stacking
• Challenges and Future Directions of AutoML