<ul><li>The author shares their experience of training a Convolutional Neural Network (CNN) for detecting pneumonia.</li><li>Key skills learned include handling class imbalance in the training set, using validation and test sets, image resizing, pixel normalization, and data augmentation.</li><li>The author explains the importance of avoiding photometric transformations in pneumonia detection and the use of ReLU activation function.</li><li>Metrics used for evaluation include accuracy, precision, recall, and loss tracking. The author invites discussion on optimizing CNN performance.</li></ul>

Training a CNN to Detect Pneumonia: Here’s What I Learned

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