Beginner's Guide to Artificial Neural Network

What is Artificial Neural Network?

They are computational methods that replicate a person's brain. They consist of nodes or neurons that process info, make decisions, and learn patterns. ANN is very good at pattern recognition.

Architecture of Artificial Neural Network

Input layer - Nodes receive input of data

Hidden layer - The intermediate layer processes data

Output layer - Produces output based on processed data

Learning Processes:

Forward Propagation - Input data fed through a network

Backward Propagation - Error is calculated

Activision Functions - Determine the output of node

Applications of ANN

• Speech recognition
• Image recognition
• Stock market prediction
• Language processing

Challenges:

Understanding decisions made by ANN is too complex. Training these networks takes a lot of computing power. 

Conclusion

As we navigate through this era of AI, understanding ANN has become important, from their structure to their applications, ANN has the potential for more and more innovation, pushing the limits of AI.