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It serves as a foundational exploration of deep learning for image-related tasks.\n\n## Project Purpose\n\nThe goal of this project is to demonstrate the application of CNNs for classifying images, utilizing well-known architectures and transfer learning methodologies. This approach allows for efficient training on smaller datasets by leveraging pre-trained models.\n\n## Key Features\n\n- **Convolutional Neural Networks**: Implementation of CNN architectures for effective feature extraction and classification.\n- **Transfer Learning**: Utilizes pre-trained models (e.g., VGG16, ResNet) to improve performance on new datasets with limited samples.\n- **Data Augmentation**: Applied techniques to enhance the dataset and improve model generalization.\n- **Metrics Monitoring**: Integrated with Weights \u0026 Biases (WandB) for tracking training metrics and visualizations.\n\n## Technologies Used\n\n- **Framework**: PyTorch\n- **Libraries**: NumPy, Matplotlib, WandB\n- **Datasets**: MNIST, Cifar10, Cifar100, FashionMNIST, ASL_Alphabet\n\n## Getting Started\n\n### Prerequisites\n\nMake sure you have the following installed:\n\n- Python\n- PyTorch\n- WandB\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsccsmartcode%2Fdeep-learning-00","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsccsmartcode%2Fdeep-learning-00","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsccsmartcode%2Fdeep-learning-00/lists"}