Convolutional Neural Networks: 100 Tips and Strategies for Building Robust CNNs

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Convolutional Neural Networks (CNNs) are widely used for image and video-related tasks in deep learning. Here are 100 tips and tricks for working with Convolutional Neural Networks:

1. Fundamentals of CNNs

1. Understand the basic architecture of a CNN, including convolutional layers, pooling layers, and fully connected layers.
2. Use convolutional layers to capture local patterns and spatial hierarchies in images.
3. Implement pooling layers to reduce spatial dimensions and computational complexity.
4. Experiment with different activation functions in convolutional layers (e.g., ReLU, Leaky ReLU, or Sigmoid).
5. Explore the impact of filter size and stride in convolutional layers on feature extraction.
6. Be mindful of the receptive field of convolutional layers and their impact on feature learning.
7. Understand the concept of weight sharing in convolutional layers for efficient feature extraction.
8. Experiment with various padding strategies in convolutional layers.
9. Be aware of the role of max pooling and average pooling in downsampling feature maps.