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model-checkpoint

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renan-siqueira / autoencoder-research

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A comprehensive toolkit built with PyTorch, designed to facilitate the training, evaluation, and visualization of autoencoders. From simple linear autoencoders to convolutional and variational architectures, this project offers an intuitive and expandable framework for anyone delving into the realm of unsupervised learning.

python docker dockerfile machine-learning computer-vision deep-learning image-reconstruction virtualization container image-processing pytorch neural-networks autoencoder dimensionality-reduction unsupervised-learning convolutional-neural-network data-augmentation variational-autoencoder model-checkpoint
  • Updated Oct 16, 2023
  • Python

ishreya09 / Skin-Cancer-Detection

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Developed a CNN model to classify skin moles as benign or malignant using a balanced dataset from Kaggle, achieving a test accuracy of 81.82% and an AUC of 89.06%. Implemented data preprocessing by resizing images to 224x224 pixels and normalizing pixel values, enhancing model performance and stability.

machine-learning dropout batch-normalization l2-regularization cnn-classification fcnn early-stopping model-checkpoint gradcam-heatmap
  • Updated Jul 29, 2024
  • Jupyter Notebook

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