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Neural networks are computing systems with interconnected nodes that work much like neurons in the human brain.
Using algorithms, they can recognize hidden patterns and correlations in raw data, cluster and classify it, and – over time – continuously learn and improve.

4,001.00
 

Syllabus

Introduction:

Basics of Neural Networks.

Basics of Artificial Neural Networks:

Characteristics, history and terminology of neural networks, models, topology and learning concepts of neural networks.

Activation and Synaptic Dynamics:

learning basics and laws, dynamics and activation models, pattern recognition and stability concepts.

Feedforward Neural Networks:

Pattern association, pattern classification, weight determination, pattern mapping and storage analysis and the technique of backpropagation algorithm.

Feedback Neural Networks:

Basics of feedback neural networks, pattern storage network analysis, stochastic networks, boltman machine and analysis of autoassociative neural networks.

Competitive Learning Neural Networks:

feedback layer and feature mapping network analysis.

Architectures for Complex Pattern and Applications of ANN:

Associative networks, neural network applications and concepts of feedforward neural networks.

Course Currilcum

          • Diploma in Neural Networks (Online Certification Courses) 02:00:00
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