UsefulLinks
Computer Science
Artificial Intelligence
Machine Learning
Machine Learning with Scikit-Learn
1. Introduction to Scikit-Learn
2. Core Scikit-Learn Concepts and API
3. Machine Learning Fundamentals
4. Data Preprocessing and Feature Engineering
5. Supervised Learning: Regression
6. Supervised Learning: Classification
7. Model Evaluation and Metrics
8. Improving Model Performance
9. Unsupervised Learning
10. Building Machine Learning Pipelines
11. Working with Text Data
12. Advanced Topics
13. Model Persistence and Deployment
14. Performance Optimization
15. Best Practices and Common Pitfalls
14.
Performance Optimization
14.1.
Computational Efficiency
14.1.1.
Algorithm Complexity
14.1.2.
Memory Usage
14.1.3.
Parallel Processing
14.2.
Scikit-learn Performance Tips
14.2.1.
Sparse Matrix Usage
14.2.2.
Efficient Data Types
14.2.3.
Warm Start Options
14.3.
Hardware Considerations
14.3.1.
CPU Optimization
14.3.2.
Memory Management
14.3.3.
GPU Acceleration Limitations
14.4.
Profiling and Benchmarking
14.4.1.
Performance Measurement
14.4.2.
Bottleneck Identification
14.4.3.
Optimization Strategies
Previous
13. Model Persistence and Deployment
Go to top
Next
15. Best Practices and Common Pitfalls