What are convolutional neural networks (CNNs) ?

Convolutional Neural Networks (CNNs) are a specialized type of deep learning neural network primarily used for processing and analyzing visual data, such as images and videos. They are designed to automatically and adaptively learn spatial hierarchies of features from input data.

---

Key Characteristics of CNNs

1. Convolutional Layers

   • Apply filters (kernels) to the input image to extract features like edges, textures, and patterns.
   • Each filter slides over the image and performs a dot product, creating a feature map.

2. Pooling Layers

   • Reduce the spatial dimensions of feature maps (downsampling) while retaining important information.
   • Common types: Max Pooling (takes the maximum value), Average Pooling.

3. Fully Connected Layers

   • After convolution and pooling, the data is flattened and passed through dense layers for classification or regression.

4. Activation Functions

   • Typically ReLU (Rectified Linear Unit) is used to introduce non-linearity.

---

Why CNNs Are Powerful

• They automatically learn features from raw data without manual feature engineering.
• They handle spatial relationships well, making them ideal for image recognition.

---

Applications

• Image Classification (e.g., recognizing cats vs dogs)
• Object Detection (e.g., detecting pedestrians in self-driving cars)
• Medical Imaging (e.g., tumor detection in MRI scans)
• Facial Recognition
• Video Analysis

---

Example Workflow

1. Input: Image of a handwritten digit.
2. Convolution: Detect edges and curves.
3. Pooling: Reduce size while keeping essential features.
4. Fully Connected Layer: Predict digit (0–9).