<ul><li>Neural Networks and Support Vector Machines (SVM) are popular algorithms for machine learning.</li><li>Neural Networks are flexible, capable of handling large datasets, and learn non-linear relationships.</li><li>SVM aims to find a hyperplane and maximize the margin, suitable for high-dimensional data and accuracy-critical tasks.</li><li>Neural Networks are used for tasks like image recognition, while SVM is useful for classifying data with small but high-dimensional datasets.</li></ul>

Neural Networks vs. Support Vector Machines (SVM): Which Model Should You Choose?

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