Artificial Neural Network: Neural Network Data Processing of a Neuron Learning and Training Samples Perceptron and Back propagation Hopefield Network

1. Artificial Neural Network
Prepared by-
Md. Mahmudul Hasan
Lecturer, Department of CSE
Pabna University of Science and Technology, Pabna

2. Neural Network
• Neuron
• Network Definition

3. Data Processing of a Neuron
• Propagation Function

4. Data Processing of a Neuron (Cont..)
• Activation Function

5. Data Processing of a Neuron (Cont..)
• Output Function

6. Learning and Training Samples
• Developing new connections,
• Deleting existing connections,
• Changing connecting weights,
• Changing the threshold values of neurons,
• Varying one or more of the three neuron
• Developing new neurons,
• Deleting existing neurons

7. Learning and Training Samples (Cont..)
• Unsupervised Learning
▫ Training set consists of input patterns
▫ Network tries by itself to detect similarities and generate
pattern classes.
• Supervised Learning
▫ Training set consists of input patterns with correct results.
▫ Network can receive a precise error vector can be returned.
▫ Steps: Entering, Forward Propagation, Comparing,
Corrections of the network, Corrections applied.

8. Perceptron and Back propagation
• Perceptron
▫ network containing a retina that is used only for data
acquisition.
▫ has fixed-weighted connections with the first neuron
layer (input layer).
▫ followed by at least one trainable weight layer
▫ One neuron layer is completely linked with the
following layer.

9. Perceptro (Cont..)

10. Perceptro (Cont..)

11. Backpropagation

12. Hopefield Network
• Neurons bias each other
• Key Properties
• Significance of Weight
• Learning procedure by codebook vectors