CNN Tutorials

Convolutional Neural Networks (CNNs) are a class of deep neural networks that are particularly effective for image recognition and processing. This page provides an overview of CNN tutorials available on our website.

Getting Started

If you are new to CNNs, we recommend starting with the following tutorials:

• Introduction to CNNs
• Building a Simple CNN

These tutorials will give you a solid foundation in understanding how CNNs work and how to build them.

Advanced Topics

Once you have a basic understanding of CNNs, you can explore more advanced topics:

• Convolutional Layers
• Pooling Layers
• Fully Connected Layers

These tutorials delve deeper into the architecture and functionality of CNNs.

Example

Here's an example of a CNN architecture:

• Input Layer: 32x32 color image
• Convolutional Layer 1: 32 filters, 5x5 kernel size
• Activation Function: ReLU
• Pooling Layer 1: 2x2 pooling
• Convolutional Layer 2: 64 filters, 5x5 kernel size
• Activation Function: ReLU
• Pooling Layer 2: 2x2 pooling
• Flatten Layer
• Fully Connected Layer 1: 512 units
• Activation Function: ReLU
• Dropout: 0.5
• Fully Connected Layer 2: 10 units (for 10 classes)
• Output Layer: Softmax

CNN Architecture

Conclusion

CNNs are powerful tools for image recognition and processing. By exploring the tutorials on our website, you can gain a deeper understanding of CNNs and their applications.