Optimization
Optimization is a crucial aspect of machine learning that involves finding the best parameters and hyperparameters for models to achieve optimal performance.
Gradient Descent
Basic Concepts
- Cost Functions
- Gradients and Derivatives
- Learning Rate
- Convergence
Variants
- Batch Gradient Descent
- Stochastic Gradient Descent
- Mini-batch Gradient Descent
- Momentum
Advanced Optimizers
- Adam
- RMSprop
- AdaGrad
- AdaDelta
Hyperparameter Tuning
Manual Tuning
- Grid Search
- Random Search
- Bayesian Optimization
- Successive Halving
Automated Tuning
- Hyperopt
- Optuna
- Ray Tune
- AutoML
Strategies
- Cross-Validation
- Hold-out Validation
- Early Stopping
- Learning Rate Scheduling
Regularization
L1 and L2 Regularization
- Lasso (L1)
- Ridge (L2)
- Elastic Net
- Group Lasso
Other Techniques
- Dropout
- Batch Normalization
- Weight Decay
- Early Stopping
Model Architecture
Neural Network Architecture
- Layer Design
- Activation Functions
- Skip Connections
- Network Depth and Width
Architecture Search
- Neural Architecture Search
- AutoML
- Evolutionary Algorithms
- Reinforcement Learning
Advanced Optimization
Second-Order Methods
- Newton's Method
- Quasi-Newton Methods
- Natural Gradient Descent
- Hessian-Free Optimization
Distributed Optimization
- Data Parallelism
- Model Parallelism
- Asynchronous SGD
- Parameter Servers
AutoML and Pipelines
AutoML
- Automated Feature Engineering
- Model Selection
- Hyperparameter Optimization
- Neural Architecture Search
ML Pipelines
- Pipeline Design
- Pipeline Optimization
- Feature Selection
- Model Stacking
Production Optimization
Model Compression
- Quantization
- Pruning
- Knowledge Distillation
- Model Compression
Deployment Optimization
- Model Serving
- Batch Inference
- Real-time Inference
- Edge Deployment