What is a convolutional neural network (CNN)?

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A Convolutional Neural Network (CNN) is a type of deep learning model specifically designed to process data with a grid-like structure, most famously images. Unlike traditional neural networks that treat inputs as flat vectors, CNNs exploit the spatial structure of data, making them highly effective for tasks like image recognition, video analysis, and even natural language processing.

🔑 Key Components of CNNs

1. Convolutional Layers

   • Apply filters (kernels) that slide over the input to detect local patterns (edges, textures, shapes).

   • Early layers learn simple features (edges), deeper layers learn complex features (faces, objects).

2. Activation Functions

   • Usually ReLU is applied after convolution to add non-linearity.

3. Pooling Layers

   • Downsample feature maps to reduce dimensionality and computation.

   • Common types: Max pooling (takes maximum value), Average pooling.

4. Fully Connected Layers

   • At the end, flatten the features and use dense layers to perform classification or regression.

5. Output Layer

   • Softmax (for multi-class classification) or sigmoid (for binary classification).

📊 Why CNNs Work Well for Images

• Local connectivity: Focuses on small regions at a time.

• Weight sharing: Same filters are applied across the image, reducing parameters.

• Translation invariance: Recognizes objects even if shifted within the image.

🚀 Applications

• Image and video recognition (e.g., face detection, self-driving cars).

• Medical imaging (tumor detection, X-rays).

• Natural language processing (text classification, sentiment analysis).

• Object detection and segmentation.

✅ In short:

A CNN is a neural network architecture that uses convolutional layers to automatically learn spatial hierarchies of features from data, making it extremely powerful for vision-related tasks. 

Read more:

What is ReLU activation?

What is sigmoid activation?

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