- AI Introduction
- Narrow AI
- Strong AI
- Nueral Network
- Deep Learning
- AI Perceptrons
- AI Historical Context
- AI Types
- Problem Solving Using AI
- AI Use Cases In Real Life
- AI Advantages Disadvantages
- AI Tools
- Cognitive AI
- Symbolic AI
- AI Ethics
- Responsible AI
- AI Multi Model Networks
- AI Genetic Algorithms
- AI Multi Agent Systems
- AI Prompt Programming
- AI Training Your Embeddings
- AI CCN Architecture
- AI Pre Trained Networks
AI CCN Architecture.
The architecture of a Convolutional Neural Network (CNN) is made up of several layers, including:
Convolutional layer
This layer uses filters, or kernels, to detect features in an image, such as edges, textures, and patterns.
Pooling layer
This layer reduces the number of parameters in the input by downsampling the feature map. It uses filters to identify parts of the image, such as edges, corners, and eyes.
Fully connected layer
This layer connects every neuron in one layer to every neuron in the next. The flattened vector from the previous layers is fed to the FC layer, where mathematical functions are performed to classify the image.