Image Classification with CNNs
- Developed a Convolutional Neural Network (CNN) to classify images from the CIFAR-10 dataset, demonstrating proficiency in deep learning and computer vision techniques.
- Achieved 88% accuracy in classifying 32x32 color images into ten object categories, showcasing the model’s effectiveness.
- Implemented image augmentation techniques to enhance performance on small datasets, demonstrating understanding of data preprocessing strategies for machine learning.
Technologies
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Utilized a comprehensive tech stack for implementation and analysis:
- Python
- Jupyter Notebooks
- TensorFlow
- Keras
- NumPy
- Pandas
- Various scientific libraries