Convolutional Neural Networks (CNNs) are a class of deep neural networks specifically designed to process and analyze visual data. They have revolutionized various fields, including computer vision, image recognition, and natural language processing. CNNs mimic the biological processes of the human visual system, making them highly effective for tasks such as object detection, image classification, and segmentation.

• Introduction to Deep Learning and Neural Networks
• Fundamentals of Convolutional Neural Networks
• Basic Concepts of Android Development
• CNN Architectures and Models
• Training CNNs
• Advanced CNN Techniques
• Applications of CNNs
• Evaluation and Performance Metrics
• Real-World Projects

• Traditional machine learning algorithms struggle with raw image data due to its high dimensionality and complexity.
• CNNs excel at capturing spatial hierarchies and local patterns within images, making them ideal for visual tasks.
• They automatically learn hierarchical feature representations from data, eliminating the need for manual feature engineering.