Neural network and artificial intelligent
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This document discusses neural networks and artificial intelligence. It defines artificial intelligence as machines programmed to think like humans, and neural networks as computational models inspired by the human brain. The document explains that neural networks are used in artificial intelligence to help machines solve complex problems. It then provides details on the basic structure and learning mechanisms of neural networks, describing how networks are composed of interconnected neurons that can learn from examples to perform tasks like pattern recognition.
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Neural network and artificial intelligent
- 2. FEATURE WE COVORED WHAT IS ARTIFICIAL INTELLIGENCE WHAT IS NEURAL NETWORK RELATION BETWEEN AI AND ANN MECHANISM OF ANN
- 3. ARTIFICIAL INTELLIGENCE Artificial intelligence (AI) refers to simulated intelligence in machines. These machines are programmed to "think" like a human and mimic the way a person acts. The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal, although the term can be applied to any machine that exhibits traits associated with a human mind, such as learning and solving problems.02
- 4. NEURAL NETWORK An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by biological nervous systems. It is composed of a large number of highly interconnected processing elements called neurons. An ANN is configured for a specific application, such as pattern recognition or data
- 5. ARTIFICIAL INTELLIGENCE VS Neural networks, called artificial neural network when used in the the field of artificial intelligence. Artificial neural networks are the neural networks which try to replicate the neural networks in human brain as a form of computational model. These computational model can be used to solve problems in the field of artificial intelligence. NEURAL NETWORK
- 6. WHY USE NEURAL NETWORKS Ability to derive meaning from complicated or imprecise data Extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques Adaptive learning Real Time Operation
- 7. WHY USE NEURAL NETWORKS Conventional computers use an algorithmic approach, but neural networks works similar to human brain and learns by example.
- 8. LEARNING PROCESS A typical neuron collects signals from others through a host called dendrites The neuron sends out spikes of electrical activity through an axon. How the Human Brain Learns?
- 9. LEARNING PROCESS At the end of each branch, a structure called synapse converts the ellectric activity into electrical effect. When a neuron receives electric effect, influence other neuron. How the Human Brain Learns?
- 10. A SIMPLE NEURON Takes the Inputs . Calculate the summation of the Inputs . Compare result with its knowledge.
- 11. FIRING RULES The firing rule is an important concept in neural networks and accounts for their high flexibility A firing rule determines how one neuron calculates whether a neuron should fire for any input pattern
- 12. HOW IT WORK For example, a 3-input neuron is taught to output 1 when the input (X1,X2 and X3) is 111 or 101 and to output 0 when the input is 000 or 001.
- 14. NATURAL LANGUAGE GENERATION SPEECH RECOGNITION AI-OPTIMISED HARDWARE BIOMETRICS VIRTUAL AGENTS DECISION MANAGEMENT MACHINE LEARNING PLATFORMS USES
- 15. SOCIAL IMPECT In medical sector AI use for developed more effective medicine, try to detect disease and some AI successfully did it. Business sector also worked with AI to make analysis market, their product, customer behavior, also for decision making.