Artificial Neural Networks (ANN) Architecture An Artificial Neural Network (ANN) is a computational framework inspired by the human brain that can learn...
An Artificial Neural Network (ANN) is a computational framework inspired by the human brain that can learn and solve problems. It is built with interconnected nodes called artificial neurons that process and transmit information. These networks consist of three types of layers:
1. Input Layer:
The first layer receives the raw data as input.
Each node in this layer receives multiple inputs and sends the combined information to the next layer.
2. Hidden Layers:
Subsequent layers are hidden from the user and are designed to learn patterns from the input data.
Each node in these hidden layers receives information from the previous layer and applies a non-linear transformation to it.
This process helps the network identify complex relationships within the data.
3. Output Layer:
The final layer produces the final output based on the processed information from the hidden layers.
This output can be a probability distribution or a single predicted value.
Key characteristics of an ANN:
Self-Organization: The network automatically discovers and organizes its own internal structure based on the data.
Parallel Processing: Information is processed in parallel across all nodes, enabling efficient solution of complex problems.
Dynamic Learning: The network continuously updates its internal structure and weights based on new data, allowing it to learn and adapt to changing situations.
Example:
Imagine an ANN trained to recognize handwritten digits. The input layer would receive the pixels of a digit image. The hidden layers would learn to identify the patterns and features of the digits, while the output layer would classify the image as a specific digit