What are Artificial Neural Networks?

What are Artificial Neural Networks?

• 1. What are Artificial Neural Networks? What are Artificial Neural Networks?
• 2. What are Artificial Neural Networks? Artificial neural networks (ANNs) are biologically inspired computer programs designed to simulate the way in which the human brain processes information. NEXT » « PREV
• 3. ANNs ANNs gather their knowledge by detecting the patterns and relationships in data and learn (or are trained) through experience, not from programming. NEXT » « PREV
• 4. Brain modeling NEXT » « PREV Brain modeling also promises a less technical way to develop machine solutions. This new approach to computing also provides a more graceful degradation during system overload than its more traditional counterparts. These biologically inspired methods of computing are thought to be the next major advancement in the computing industry.
• 5. Research Research shows that brains store information as patterns. Some of these patterns are very complicated and allow us the ability to recognize individual faces from many different angles. NEXT » « PREV
• 6. process The process of storing information as patterns, utilizing those patterns, and then solving problems encompasses a new field in computing. NEXT » « PREV
• 7. Field This field, as mentioned before, does not utilize traditional programming but involves the creation of massively parallel networks and the training of those networks to solve specific problems. Also utilizes words very different from traditional computing, words like behaving, react, self-organize, learn, generalize, and forget. NEXT » « PREV
• 8. The various applications of ANNs The various applications of ANNs can be summarised into classification or pattern recognition, prediction and modeling. Supervised associating networks can be applied in pharmaceutical fields as an alternative to conventional response surface methodology. NEXT » « PREV
• 9. The various applications of ANNs Unsupervised feature-extracting networks represent an alternative to principal component analysis. Non-adaptive unsupervised networks are able to reconstruct their patterns when presented with noisy samples and can be used for image recognition. NEXT » « PREV
• 10. The various applications of ANNs NEXT » « PREV
• 11. SOURCE SOURCE: www.psych.utoronto.ca/users/ai/cache/neural2.html, www.sciencedirect.com/science/article/abs/pii/S0731708599002721 THANK YOU