What Model Architecture Meaning, Applications & Example
Structural design of a neural network or ML model.
What is Model Architecture?
Model architecture refers to the structure of a machine learning model, including how it is organized and how different components interact. It defines the layers, connections, and operations used to transform input data into output predictions. The architecture directly impacts the model’s performance and suitability for specific tasks.
Types of Model Architecture
- Feedforward Neural Network: A simple architecture where data flows in one direction, from input to output, without loops.
- Convolutional Neural Network (CNN) : Used for image-related tasks, CNNs apply convolutional layers to extract features from images.
- Recurrent Neural Network (RNN) : Designed for sequential data, RNNs have loops that allow information to persist between time steps.
- Transformer: A model architecture for natural language processing (NLP) that uses attention mechanisms to process sequences in parallel.
Example of Model Architecture
In a Convolutional Neural Network (CNN) for image classification :
- Input Layer: The raw image data.
- Convolutional Layers: Extract features from the image.
- Pooling Layers: Reduce the dimensionality of features.
- Fully Connected Layers: Perform classification based on extracted features.
Each layer works together to transform the raw image into a predicted label.