What is the difference between SVM and Neural Networks?

Can you explain the difference between SVM and neural networks?

Sure! Support Vector Machines (SVM) are supervised learning models used for classification and regression tasks, while neural networks are a set of algorithms modeled loosely after the human brain, used for a variety of tasks including image and speech recognition.

What are the main advantages of using SVM over neural networks?

SVMs are effective in high-dimensional spaces and are memory efficient. They also work well with smaller datasets. In contrast, neural networks require larger datasets to perform well and can be more prone to overfitting.

Are there specific scenarios where one is preferred over the other?

Yes, SVMs are often preferred for smaller datasets with clear margins of separation, while neural networks excel in complex tasks like image and speech recognition where large amounts of data are available.

Can you give an example of a real-world application for each?

Certainly! SVMs are commonly used in text classification, like spam detection, while neural networks are widely used in self-driving cars for object detection.