Lecture 70: Radial Basis Function (RBF) - Continued | SVM
Автор: ElhosseiniAcademy
Загружено: 2024-10-09
Просмотров: 212
Join us for an in-depth lecture on Support Vector Machines (SVM), one of the most powerful algorithms in machine learning! 🧠✨
What We'll Cover:
The Impact of Gamma on Overfitting & Underfitting 🔄
Discover how the gamma parameter influences the model's flexibility.
Learn to balance gamma to prevent overfitting and underfitting.
Specialized Kernels 🧩
Dive into custom kernels and their role in handling complex data.
Explore when and how to use different kernel functions effectively.
Numerical Examples 📊
Work through hands-on examples to solidify your understanding.
See theory put into practice with real data.
Coding Session 💻
Implement SVMs using popular libraries.
Get familiar with SGDClassifier, LinearSVC, SVC, and more.
Use Cases & Model Selection 📈
Training Size Considerations: Understand which SVM variant to use based on your dataset size.
Out-of-Core Support 🖥️: Learn about SVMs that handle data too large for memory.
Convergence & Computational Complexity ⏳: Grasp how different algorithms converge and their computational demands.
High-Dimensional Data 🌌: Tackle challenges and solutions in high-dimensional spaces.
SVM as a Regressor & The Epsilon-Tube 📐
Extend SVM concepts to regression problems.
Understand the epsilon-tube and its impact on model tolerance and error.
Why You Should Attend:
This lecture bridges theory and practice, equipping you with the knowledge to apply SVMs effectively in various scenarios. Whether you're dealing with large datasets, high-dimensional spaces, or regression problems, you'll gain valuable insights and practical skills. 🚀
#MachineLearning #SVM #DataScience #AI #CodingSession #Regression #KernelTricks #BigData #HighDimension #Overfitting #Underfitting #EpsilonTube 💡🤖

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