This document provides a detailed overview of Convolutional Neural Networks (CNNs), a powerful type of deep learning architecture widely used in image recognition, computer vision, and other related fields. It covers the fundamental concepts, layers, and applications of CNNs, making it an ideal resource for beginners and those looking to deepen their understanding.
Key Topics Covered
- What are CNNs and why are they effective?
- Convolutional layers: Filters, kernels, and stride
- Pooling layers: Max pooling and average pooling
- Activation functions: ReLU, sigmoid, and tanh
- Fully connected layers
- Applications of CNNs in image recognition
- Training and optimization of CNNs
We encourage you to explore the document thoroughly. For more advanced topics and deeper dives, consider consulting further research and documentation.