Convolutional Neural Networks (CNN) for Computer Vision represent a powerful approach in image analysis. The process begins with a comprehensive understanding of Artificial Neural Networks (ANN) as a foundational concept. Data preprocessing plays a crucial role, involving tasks like image normalization and augmentation to enhance model robustness. Leveraging the TensorFlow framework, the model is constructed, encompassing convolutional layers for feature extraction and fully connected layers for classification. Evaluation metrics such as accuracy and loss are employed to assess the model's performance. Finally, deployment involves integrating the trained CNN into real-world applications, ensuring its seamless integration and functionality for image recognition tasks.

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