CNNs are foundational in image recognition, leveraging layered structures to extract hierarchical features. Here's a breakdown of their core components:

1. Convolutional Layers

These layers apply filters (kernels) to input data, detecting spatial patterns like edges or textures.

Convolutional_Layer
Each filter slides across the input, performing element-wise multiplication and summing results to produce feature maps. 🖼️

2. Pooling Layers

Used to reduce spatial dimensions, commonly max pooling or average pooling.

Pooling_Layer
This helps control overfitting and speeds up computation. ⚡

3. Activation Functions

Non-linear functions like ReLU (Rectified Linear Unit) introduce complexity for pattern recognition.

ReLU_Activation
ReLU is widely used due to its simplicity and effectiveness. 📈

4. Fully Connected Layers

Final layers that classify features into output categories.

Fully_Connected_Layer
They connect every neuron in the previous layer to all neurons in the current layer. 🔄

5. Key Advantages

  • Parameter Sharing: Reduces computation by reusing filters across the input.
  • Translation Invariance: Detects features regardless of their position.
  • Hierarchical Feature Learning: From local patterns to global context.

For a deeper dive into CNN implementation, check out our CNN Tutorial. 📘
Explore more about neural network fundamentals at /neural-network-basics. 🌐