CNN.md

Convolutional Neural Netowrks

What is a CNN?

• A Convolutional Neural Network is a type of deep neural network designed for processing strcutred grid data like images.
• Primarily used in Image Recognition,Object Detection, Computer Vision Tasks etc...

Key Components of a CNN

1. Convolutional Layer:

   • A Convolutional Layer is the core component of a CNN.
   • It applies filters to the input images,sliding them across the image ans capture features from the images like edges and textures etc..
   • Each filter produced a feature map that detects specific patterns in the data.

2. Pooling Layer:

   • Reduces the spatial dimensions of the feature maps helpig to reduce time and calculation complexity
   • Types of Pooling:
     • Max Pooling: Takes maximum value in a smaller window.
     • Average Pooling: Takes average of the values in the smaller window.
   • Helps make the model invariant to small translations in the input data.

3. Activation Functions:

   • Introduces non-linearity by converting negative values to zero.
   • Ensures that the model learns complex patterns and relationships in the data.

4. Fully Connected Layer:

   • The final layer(s) of a CNN that connects every node in one layer to every node in the next layer.
   • Combines the features extracted by convolutional and pooling layers to produce the final classification or prediction.

Workflow of CNNs:

How Does a CNN Work?

1. Input Layer: The input to a CNN is typically an image represented as a matrix of pixel values.

2. Convolution Operation: Filters (small matrices) slide over the input image to detect features like edges, corners, or textures.

3. Feature Maps: The result of the convolution operation produces feature maps that highlight the presence of specific patterns or features in different regions of the input.

4. Pooling: The feature maps are downsampled to reduce the size, keeping the most important information (e.g., using max pooling).

5. Flattening and Fully Connected Layers: The output is flattened into a vector, which is fed into fully connected layers for classification or regression tasks.

6. Output Layer: Produces the final prediction, such as classifying an image into categories.

Applications of CNN's:

• Image Classification: Recognizing objects within images
• Object Detection: Locating and identifying multiple objects within an image.
• Facial Recognition: Detecting and recognizing human faces.
• Medical Imaging: Analyzing X-rays, MRIs, and other medical images for diagnostic purposes.