dataaugmentation

Data Augmentation Techniques for Computer Vision

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Computer vision models are notoriously data-hungry. While traditional machine learning algorithms might perform well with hundreds or thousands of examples, deep learning models for image recognition, object detection, and segmentation typically require tens of thousands or even millions of labeled images to achieve state-of-the-art performance. This creates a significant challenge: acquiring and labeling massive datasets … Read more

What is SMOTE in Data Augmentation?

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In the world of machine learning and data science, one of the most persistent challenges practitioners face is dealing with imbalanced datasets. When certain classes in your dataset are significantly underrepresented compared to others, traditional machine learning algorithms often struggle to learn meaningful patterns from the minority classes. This is where SMOTE (Synthetic Minority Oversampling … Read more

Data Augmentation Techniques for Tabular Data

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Data augmentation has revolutionized computer vision and natural language processing, but its application to tabular data remains less explored despite being equally transformative. While image augmentation involves rotating, cropping, or adjusting brightness, tabular data augmentation requires more nuanced approaches that preserve the underlying statistical relationships between features while generating meaningful synthetic samples. In the realm … Read more