Optimizing Deep Learning for Imbalanced Image Data
Optimizing Deep Learning for Imbalanced Image Data
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The ShiftGuard10 Project presentation provides a comprehensive overview of a machine learning initiative focused on image classification. Beginning with an introduction to the project's objectives and challenges, it details the dataset comprising 29,400 training and 7,600 test images, addressing class imbalance issues. Techniques such as data augmentation and normalization are discussed, alongside the ResNet model architecture that tackles gradient issues. The presentation covers strategies...