Introduction
An introduction to Support Vector Machines (SVM), a powerful algorithm for classification and regression tasks.
Theory
Dive into the theory behind SVMs, including the concepts of hyperplanes.
scikit-learn Example
Build a leakage-safe multiclass SVM pipeline and tune scaling, margin, and kernel parameters inside training folds.
TensorFlow Guidance
Understand the boundary between TensorFlow gradient training and a maintained support-vector-machine solver.
PyTorch Guidance
Choose a maintained SVM solver while using PyTorch where tensor and representation learning are appropriate.
Common Mistakes & Best Practices
Avoid common mistakes in SVM implementation and optimize your models with best practices.
Comparison with Other Algorithms
Compare SVMs with other machine learning algorithms to understand their unique advantages.