Artificial Intelligence- Neural Networks
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Artificial Neural Networks (ANNs) aim to replicate the human brain's neural connections to enable computers to learn and make decisions similarly to humans. ANNs consist of interconnected nodes that function like biological neurons, processing input data through weighted links and adjusting those weights to learn. The document discusses the basic structure, types of ANNs, and their applications, noting the distinct characteristics of feedforward and feedback networks.
Artificial Intelligence- Neural Networks
- 1. Swipe Artificial Intelligence - Neural Networks AI
- 2. Yet another research area in AI, neural networks, is inspired from the natural neural network of human nervous system. An artificial neural network is an attempt to simulate the network of neurons that make up a human brain so that the computer will be able to learn things and make decisions in a humanlike manner. ANNs are created by programming regular computers to behave as though they are interconnected brain cells. AI- Neural Networks
- 3. Basic Structure of ANNs The idea of ANNs is based on the belief that working of human brain by making the right connections, can be imitated using silicon and wires as living neurons and dendrites. The human brain is composed of 86 billion nerve cells called neurons. They are connected to other thousand cells by Axons. Stimuli from external environment or inputs from sensory organs are accepted by dendrites. These inputs create electric impulses, which quickly travel through the neural network. A neuron can then send the message to other neuron to handle the issue or does not send it forward.
- 4. ANNs are composed of multiple nodes, which imitate biological neurons of human brain. The neurons are connected by links and they interact with each other. The nodes can take input data and perform simple operations on the data. The result of these operations is passed to other neurons. The output at each node is called its activation or node value.
- 5. Each link is associated with weight. ANNs are capable of learning, which takes place by altering weight values. The following illustration shows a simple ANN:-
- 6. Types of Artificial Neural Networks FeedForward and Feedback. There are two Artificial Neural Network topologies :-
- 7. FeedForward ANN In this ANN, the information flow is unidirectional. A unit sends information to other unit from which it does not receive any information. There are no feedback loops. They are used in pattern generation/recognition/classification. They have fixed inputs and outputs.
- 8. FeedBack ANN Here, feedback loops are allowed. They are used in content addressable memories.
- 9. AI - Working of ANNs AI - issues and Terminology Python - Data structure Topics for next Post Stay Tuned with