The document outlines the concept of backpropagation in neural networks, explaining its functions, benefits, and applications, such as in speech and character recognition. It describes the structure of neural networks, including different layers and the algorithm's role in error testing and weight updates. Additionally, it promotes learning opportunities in big data through courses offered by Simplilearn.

1. Agenda
• What is Backpropagation?
• What is Backpropagation in a Neural network?
• How does Backpropagation work?
• Benefits of Backpropagation
• Applications of Backpropagation

3. Which among the following is not a layer of a neural network?
D. Hidden Layer
A. Input Layer
C. Propagation
Layer
B. Output Layer

4. Backpropagation is an algorithm which is created to test errors which will
travel back from input nodes to output nodes.
Input Nodes Output Nodes
Neutral Network

5. It is similar to a Biological network that contains neuron coupled with each
other.
Input Layer Output Layer
Hidden Layer
Weight

6. a = Activation Function
a
w4
w2
w1
w3
w5
w6

7. a
w1
w2
w3
w4
w5
w6
} Loss
Calculation

8. Input Layer a
w1
w2
w3
w4
w5
w6
w1,w2,w3….w6=weight
Output Layer

9. a
w1
w2
w3
w4
w5
w6
H1=x1×w1+x2×w2+b1

10. a
w1
w2
w3
w4
w5
w6
Update

11. a
w1
w2
w3
w4
w5
w6
Update

12. Which among the following is not a layer of a neural network?
D. Hidden Layer
A. Input Layer
C. Propagation
Layer
B. Output Layer

13. Benefits
Quick Versatile No extra functions

14. Applications
Speech Recognition Character Recognition
Signature Recognition Face Recognition

15. Conclusion
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